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Driving Comfort, Enjoyment, and Acceptance of Automated Driving - Effects of Drivers’ Age and Driving Style Familiarity
Abstract
Automated driving has the potential to improve the safety and efficiency of future traffic and to extend elderly peoples’ driving life, provided it is perceived as comfortable and joyful and is accepted by drivers. Driving comfort could be enhanced by familiar automated driving styles based on drivers’ manual driving styles. In a two-stage driving simulator study, effects of driving automation and driving style familiarity on driving comfort, enjoyment, and system acceptance were examined. Twenty younger and twenty older drivers performed a manual and four automated drives of different driving style familiarity. Acceptance, comfort, and enjoyment were assessed after driving with standardised questionnaires, discomfort during driving via handset control. Automation increased both age groups’ comfort, but decreased younger drivers’ enjoyment. Younger drivers showed higher comfort, enjoyment and acceptance with familiar automated driving styles, whereas older drivers preferred unfamiliar, automated driving styles tending to be faster than their age-affected manual driving styles.
Practitioner Summary
Automated driving needs to be comfortable and enjoyable to be accepted by drivers, which could be enhanced by driving style individualisation. This approach was evaluated in a two-stage driving simulator study for different age groups. Younger drivers preferred familiar driving styles, whereas older drivers preferred driving styles unaffected by age.
... The technological aspects of automated cars (ACs) are progressing at a rapid pace, and the availability to the general public of vehicles with higher levels of automation draws closer. Their development is expected to reduce the number of accidents, traffic congestion, and even the carbon footprint of this type of transport [1]. Attention is now being partially shifted from the sheer technological feasibility to the question of how these ACs should behave in order to optimize their interaction with humans, as this interaction is considered a key issue to ensure that their expected benefits can be realized [2]. ...
... Conversely, an unfamiliar automated driving-style could violate these comfort zones, for instance by engaging in maneuvers considered as unsafe or uncomfortable. The studies on this subject seem to be in line with these assumptions, as they have shown that in most situations, personalized automated driving-styles are preferred [1,5,6], more accepted [6,7], and are perceived as more trustful [6,8], comfortable [6,8] and safe [6]. ...
... The results of this study have shown that the preference for the personalized automated driving-style (compared to artificial alternatives) in over-taking situations was more pronounced when there was a vehicle approaching on the left lane at 160 km/h than when no vehicle was travelling on the left lane. Another study has also shown that while young participants preferred a familiar automated driving-style, older participants preferred an unfamiliar one, tending to be faster than their age-affected manual driving-style [1]. Other studies have also shown that the appreciation of automated driving-style could vary according to driving situations such as driving in intersections [9], in adverse weather conditions [10], or in heavy traffic [10]. ...
Studies investigating the question of how automated cars (ACs) should drive converge to show that a personalized automated driving-style, i.e., mimicking the driving-style of the human behind the wheel, has a positive influence on various aspects of his experience (e.g., comfort). However, few studies have investigated the fact that these benefits might vary with respect to driver-related variables, such as trust in ACs, and contextual variables of the driving activity, such as weather conditions. Additionally, the context of intermediate levels of automation, such as SAE level 3, remains largely unexplored. The objective of this study was to investigate these points. In a scenario-based experimental protocol, participants were exposed to written scenarios in which a character is driven by a SAE level 3 AC in different combinations of conditions (i.e., types of roads, weather conditions and traffic congestion levels). For each condition, participants were asked to indicate how fast they would prefer their AC to drive and how fast they would manually drive in the same situation. Through analyses of variance and equivalence tests, results showed a tendency for participants to overall prefer a slightly lower AC speed than their own. However, a linear regression analysis showed that while participants with the lowest levels of trust preferred an AC speed lower than theirs, those with the highest levels preferred an AC speed nearly identical to theirs. Overall, the results of this study suggest that it would be more beneficial to implement a personalization approach for the design of automated driving-styles rather than a one for all approach.
... Therefore, it is crucial to parametrize acceptability in automated driving (selected here to describe acceptance related to the use of AVs, distinguished from a priori acceptance) and explore the factors that affect it. Simulator findings by Hartwich et al. (2018) suggest that automated driving will need to be comfortable and enjoyable in order to be acceptable to the user. Traditional definitions of comfort as perceived by a car user (e.g., Pennestri et al., 2005) usually refer to the vehicle's interior design and chassis shapes and materials and do not consider the interactions with the road and traffic environment. ...
... Traditional definitions of comfort as perceived by a car user (e.g., Pennestri et al., 2005) usually refer to the vehicle's interior design and chassis shapes and materials and do not consider the interactions with the road and traffic environment. Nevertheless, the loss of controllability in automated driving will introduce additional comfort measures (Elbanhawi et al., 2015), such as psychological driving comfort which can refer to the feeling of naturalness and apparent safety (Elbanhawi et al., 2015;Hartwich et al., 2018). ...
... The study identified two components that described overtaking related preferences, which were described as "dependability" and "agreeability" of the overtake. The two components appear to relate to suggested elements of psychological comfort in automated driving, the feelings of apparent safety and naturalness (Elbanhawi et al., 2015;Hartwich et al., 2018). Dependability referred to behaviours that conveyed to the participant the feeling of a safe operation. ...
Future user acceptance will be a requirement for the AVs to accomplish their estimated safety benefits, highlighting the importance of acceptable driving behaviour. This study aims to investigate the parameters that affect the acceptability of highly automated overtaking. 237 respondents participated in a video based online survey, rating different motorway flying overtaking scenarios based on their preferences. The scores were analysed using a variety of methods (statistical tests, Principal Component Analysis, Linear Mixed Models). Long pull-out distances and manoeuvre duration values, as well as lower speeds were preferred by the participants, with some limited impact of the driving situation. Overall, behaviour simulating an average, cautious human driver is likely to positively influence acceptability and suggests the value of further research on context-adaptive automated driving to account for subjective risk perception. These findings can contribute towards user-centred systems that assist or autonomously perform overtaking manoeuvres, supporting their uptake and thus the realisation of their safety benefits.
... Although there is currently no commonly agreed definition for comfort in this context, some suggestions exist. Under the context of automated driving, Hartwich et al. (2018) summarised driving comfort as 'a subjective, pleasant state of relaxation given by confidence and an apparently safe vehicle operation, which is achieved by the removal or absence of uneasiness and distress' (p. 1019). ...
... One, relatively unexplored, concept in this context is 'naturalness' of the AV's driving behaviour, which has been linked to the familiarity of the AV's manoeuvres, for the user. Here, the familiarity of AV movements, rendered by mimicking human-like vehicle controls, is expected to fulfil human users' anticipation of an AV's behaviours, and result in positive subjective feedback (Butakov & Ioannou, 2015;Hartwich et al., 2018). Moreover, Elbanhawi et al. (2015) suggest that naturalness of automated driving is an important determinant of comfort. ...
... Moreover, Elbanhawi et al. (2015) suggest that naturalness of automated driving is an important determinant of comfort. However, some empirical studies have shown that familiar automated driving manoeuvres do not always lead to higher subjective comfort (Hartwich et al., 2018), which suggests that more knowledge is needed on the link between these two concepts, since they will likely contribute to acceptance of future AVs. ...
Objective
This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator.
Background
Comfort and naturalness play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural.
Method
A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity.
Results
Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers.
Conclusion
Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers.
Application
Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems.
... Under the fourth level of autonomy proposed by NHSTA [44], all driving tasks are carried out by the vehicle and all occupants are considered to be passengers. Thus, it is anticipated that people who drive for fun will be less likely to choose AVs [45]. ...
... A fully operational AV operates as an independent entity, which means the driver is no longer confronted with challenging situations and is not able to utilize their skills. It was observed that respondents who enjoy driving are more inclined to choose conventional cars (β = 0.77), which is consistent with previous research [45,62]. ...
Autonomous vehicles (AVs) have a number of potential advantages, although some research indicates that this technology may increase dependence on private cars. An alternative approach to bringing such technology to market is through autonomous taxis (ATs) and buses, which can assist in making transportation more sustainable. This paper aims at examining the role of attitudinal, travel-related, and individual factors in preferences for a modal shift from conventional cars toward ATs and exclusive-lane autonomous buses (ELABs), exploring the existence of heterogeneity and its possible sources. The proposed mixed logit model with a decomposition of random coefficients uses 1251 valid responses from a stated preference survey distributed in Tehran, in 2019. Results show that there is significant taste variation among individuals with respect to ATs’ travel costs, ELABs’ travel times, and walking distances to ELAB stations. Furthermore, exploring the sources of heterogeneity indicates that women are more sensitive to ATs’ travel costs and walking distances to ELAB stations while they are less sensitive to ELABs’ travel times. Moreover, travel time in discretionary activities reduces the utility of ELABs more than it does in mandatory activities. Transportation authorities can use these findings to establish more effective policies for the successful implementation of AVs.
... It is therefore a big challenge to better manage the interdependence between humans and cars, to avoid this irony of automation (Bainbridge, 1983), as well as to reduce some dangerous or discomfortable experiences resulting from automation surprise (Sarter, Woods & Billing, 1997). Indeed, the discrepancies between human driving styles and car driving modes, in terms of road perception, diagnosis and driving actions could lead to risky situations (difficulty to regain control), but also to unpleasant situations (where the drivers do not understand or trust the choices and the behaviour of the car, Hartwich et al., 2018). ...
... Following recent studies (Hartwich et al., 2018), another key aspect to be addressed could also be the discomfort caused by the gaps between the human cognitive control preferences (like driving style in cars), and the dynamic behaviour implemented by autonomous system. We have started to explore this question through the master internship of Mathieu Legendre. ...
The Human-Autonomy Teaming paradigm (HAT) has recently emerged to model and design hybrid teams, where a human operator must cooperate with an artificial agent, able to independently evolve in dynamic and uncertain situations. An important challenge in HAT is to transform autonomous systems into better teammates, capable of joining humans in highly interdependent activities. The presented works explore two main avenues, supported by industrial collaborations (in the domain of transportation and industrial systems), academic partnerships (especially with South Australian universities), and with the supervision PhD students. The first axis deals with the monitoring of cognitive states, to equip the machine with an ability to detect when human face difficulties. To address this question, a global approach is proposed to classify operators mental workload from the fusion of multisourced physiological and behavioral data. The second axis focused on the mechanisms for adapting human-autonomy teaming, making machine more compatible with human. Two kinds of solution are explored. One focused on the offline enhancement of the know-how-to-cooperate of machines, with the aid of CWA method and MDE techniques. The other deals with online adaptation of human-machine cooperation, where autonomous system can be considered inside the team - as a teammate - or above the team-as a coach. Finally, new research directions are opened, supported by ongoing initiatives in France and abroad. These perspectives relate to the consolidation of a multilevel approach for cognitive state monitoring, the building of a transparent dialogue between human and autonomy, a deeper consideration of transitional and longitudinal situations in HAT, and the scale-up challenge of studying HAT with human teams.
... Surprisingly, and despite an extended interest in the notion of discomfort (e.g. Hartwich, Beggiato, and Krems 2018), there is no generally accepted definition for the term, as some definitions prove contradictory, partial, or mutually irreconcilable (cf., de Looze, Kuijt-Evers, and Van Dieën 2003). We begin our work therefore, by reviewing the literature on discomfort, and then we proceed to integrate the extant spectrum of definitions to generate a comprehensive model of discomfort, which is applicable across multiple domains. ...
... To compare with the wish for avoidance-of-pain, we must realize what stimuli impact it, and how individual differences affect the perceived stimuli. For example, individual characteristics such as age have an impact in adjusting to these stimuli (Hartwich, Beggiato, and Krems 2018) due to the innate differences here, between the bodily capabilities of younger and older people. ...
Interaction with and dependency on intelligent autonomous systems, may bring about feelings such as discomfort or fear. Users’ willingness to accept new technologies can be hampered by unwanted emotions like discomfort, making the study of the onset of discomfort essential for future technology design and implementation. Interest in discomfort has been growing but agreed-upon definitions or models are still wanted. Here, we present a theoretical model of discomfort predicated upon existing models and definitions. Our model emphasizes internal mental processes that guide the formation of discomfort. Specifically, we specify how environmental stimuli are linked to personal needs and expectations, and how that gap between internal and external factors contributes to discomfort. We conclude with a practical example of how our model can apply to the design of autonomous vehicles.
... The majority of these studies have conducted evaluations after participants complete the whole experimental drive. For example, [13] asked participants to rate their perceived comfort and enjoyment after each drive using a questionnaire composed of 32 items. [6] provided a one-item rating scale after each trial for participants to evaluate if the deceleration and lane-changing manoeuvres of automated vehicles were comfortable. ...
... The results showed that, for both methods, regardless of the number of response options, a similar trend was found, with participants providing more positive, than negative, evaluation for all controllers, in both studies. This finding is in line with that of [13]. Compared to the wide range of response options provided by the Likert scale, a binary method only provides two options for participants to express their evaluation. ...
This paper compared two different methodologies, used in two driving simulator studies, for real-time evaluation of comfort imposed by the driving style of different Automated Vehicle (AV) controllers. The first method provided participants with two options for assessing three different AV controllers. Participants rated each controller in terms of whether or not it was comfortable/safe/natural, when it navigated a simulated road. The evaluation was either positive (yes) or negative (no), indicated by pressing one of two buttons on a handset. In the second study, an 11-point Likert-type scale (from-5 to +5) was used to evaluate the extent to which a controller's driving style was "comfortable" and/or "natural", separately. Participants provided this evaluation for three different AV controllers. Here, they were instructed to utter a number from the scale, at designated points during the drive. To understand which method is better for such evaluations, we compared the data collected from the two studies, and investigated the patterns of data obtained for the two methodologies. Results showed that, despite the multiple response options provided by the 11-point scale, a similar pattern was seen to that of the binary method, with more positive responses provided for all controllers. The Likert scale is useful for identifying differences because of the multiple levels of responses. However, allowing people to present their ratings as often as they want, also makes the binary technique useful for such evaluations.
... Driving is a complex task and preferences for the AV's behavior can vary for each individual user [9] [10]. Differences in drivers' preferences lead to the assumption that AVs and their driving behaviour should be adaptable to the user's preferences to increase trust and acceptance of the technology [15][16] [17]. For turning maneuvers at intersections, drivers have individual preferences for the preferred gap sizes [11][12] [13]. ...
... Preferences regarding the driving behavior of an autonomous vehicle can vary significantly between individuals. These preferences could be estimated by the ATM via demographic data, such as gender, age [13] [15] or specified directly by the individual [17]. Based on the results of this case-study, we argue that the individual preferences and a human factor perspective should be incorporated in the design and development of ATM and ITS. ...
Autonomous traffic management (ATM) allows autonomous vehicles to cooperate and can thus lead to an increased traffic efficiency, enhanced traffic safety and decreased ecological footprint of traffic. For the adoption of autonomous vehicles (AVs), humans have to accept and trust them, but so far, the research field of ATM has focused mainly on the aspired safety and efficiency gain instead of an increase in trust and user acceptance of the technology. In this paper, we discuss the need for a human-centered approach for ATM.For demonstration purposes we designed a turning scenario at an urban intersection with just AVs. The traffic scenario was then simulated with the traffic simulation package SUMO. We implemented a simple ATM system that can interact and manage the behavior of the traffic participants. We present the results of the traffic simulation and the proposed ATM and discuss why the human factors perspective should be considered for the design of ATM. We argue how a human-centered ATM could be used to find a good trade-off between safe and efficient traffic and a comfortable and trustworthy user experience.
KeywordsTraffic management systemsAutonomous vehiclesHuman-centered designTraffic simulations
... Appropriately designed automated driving styles contribute to increase driving comfort [1] and the general acceptance of automated driving [2]. Besides the tactical decision-making process and question of when which driving maneuver is performed automatically, the operational performance of these maneuvers plays an important role. ...
... It is conceivable that the actual maximum longitudinal accelerations are achieved before or after the evaluated non-automated lane changes and thus do not lie within the recorded highway sections. As a consequence, the actual maximum longitudinal accelerations would exceed the values presented in Section 4. In addition, various studies (e. g. [2,6]) indicate that automated driving styles should not necessarily correspond to human driving styles. For this reason, the high degree of agreement between the automated longitudinal dynamics and non-automated longitudinal dynamics does not mean that increasing acceleration would not optimize driving comfort for automated driving. ...
Lane changes represent central driving maneuvers on highways and are frequently linked to acceleration maneuvers. For automated driving, previous studies have addressed the issue of appropriate longitudinal accelerations for vehicle occupants. However, these investigations only considered pure longitudinal acceleration maneuvers and have neglected potential influence of lane changes on driving experience. For this reason, this paper presents an evaluation of longitudinal accelerations during non-automated and automated lane changes and compares the results with previous studies. Based on this, the usefulness of further human-centered research on longitudinal accelerations during automated lane changes is discussed and recommendations for a future study are proposed.
... The majority of these studies have conducted evaluations after participants complete the whole experimental drive. For example, [13] asked participants to rate their perceived comfort and enjoyment after each drive using a questionnaire composed of 32 items. [6] provided a one-item rating scale after each trial for participants to evaluate if the deceleration and lane-changing manoeuvres of automated vehicles were comfortable. ...
... The results showed that, for both methods, regardless of the number of response options, a similar trend was found, with participants providing more positive, than negative, evaluation for all controllers, in both studies. This finding is in line with that of [13]. Compared to the wide range of response options provided by the Likert scale, a binary method only provides two options for participants to express their evaluation. ...
This paper compared two different methodologies, used in two driving simulator studies, for real-time evaluation of comfort imposed by the driving style of different Automated Vehicle (AV) controllers. The first method provided participants with two options for assessing three different AV controllers. Participants rated each controller in terms of whether or not it was comfortable/safe/natural, when it navigated a simulated road. The evaluation was either positive (yes) or negative (no), indicated by pressing one of two buttons on a handset. In the second study, an 11-point Likert-type scale (from-5 to +5) was used to evaluate the extent to which a controller's driving style was "comfortable" and/or "natural", separately. Participants provided this evaluation for three different AV controllers. Here, they were instructed to utter a number from the scale, at designated points during the drive. To understand which method is better for such evaluations, we compared the data collected from the two studies, and investigated the patterns of data obtained for the two methodologies. Results showed that, despite the multiple response options provided by the 11-point scale, a similar pattern was seen to that of the binary method, with more positive responses provided for all controllers. The Likert scale is useful for identifying differences because of the multiple levels of responses. However, allowing people to present their ratings as often as they want, also makes the binary technique useful for such evaluations.
... El estudio del comportamiento de un conductor al volante y la posición que este adopta, ha sido estudiada con anterioridad [1] debido a que su estudio puede prevenir posibles lesiones o accidentes. Otros autores han lidiado tambíen con la materia; algunos centrándose en el asiento del conductor [2,3] y otros centrándose en el propio conductor [4][5][6]. También existen estudios centrados en un grupo de conductores concreto, por ejemplo el de los taxistas [7,8]. A parte, algunos autores han considerado el uso de herramientas alternativas a las tradicionales, como la lógica difusa [9][10][11], ya que su uso permite llevar a cabo el estudio de sistemas complejos teniendo en cuenta la relación entre las diferentes variables mediante la definición de reglas heurísticas [9,10,12,13]. ...
Saber como es el comportamiento al volante de un conductor es un tema muy estudiado a fecha de hoy, pues conociéndolo sería posible adelantarse y predecir las diferentes repercusiones que podrían tener sus acciones, así como la posibilidad de evitar lesiones por un mal uso o hasta posibles accidentes. Diversos autores han empleado numerosos sistemas, como la lógica difusa, para crear modelos del conductor, los cuales permitirían detectar los niveles de fatiga o corregir la postura adoptada. El presente artículo se va a centrar en la presentación de un modelo realizado mediante lógica difusa, el cual predice la fuerza que un usuario aplicaría sobre el pedal de embrague a la hora de conducir un vehículo, partiendo de aquellos factores antropométricos más influyentes. De este modo, en un futuro, podría predecirse el estado de fatiga del conductor, pudiéndose aconsejar mejoras y de esta forma evitar posibles lesiones a futuro. Para ello se han realizado una serie de pruebas de conducción en un entorno real con un total de 28 voluntarios. Mediante dichas pruebas se han registrado las fuerzas aplicadas sobre el pedal, además de diferentes medidas antropométricas que han permitido la creación del sistema planteado.
... On this basis, drivers should be able to inform their ICVs how they want to be driven in order to enhance their driving experience inside them. This statement is based on the fact that "driving enjoyment" is one of the main determinants affecting consumers' future desire to use and accept ICVs [26,27]. Additionally, ICV's operator system needs a high amount of information from the external changing environment [28][29][30]. ...
Intelligent connected vehicles (ICVs) constitute a transformative technology attracting immense research effort and holding great promise in providing road safety, transport efficiency, driving comfort, and eco-friendly mobility. As the driving environment becomes more and more “connected”, the manner in which an ICV is driven (driving style) can dynamically vary from time to time, due to the change in several parameters associated with personal traits and with the ICV’s surroundings. This necessitates fast and effective decisions to be made for a priori identifying the most appropriate driving style for an ICV. Accordingly, the main goal of this study is to present a novel, in-vehicle autonomous decision-making functionality, which enables ICVs to dynamically, transparently, and securely utilize the best available driving style (DS). The proposed functionality takes as input several parameters related to the driver’s personal characteristics and preferences, as well as the changing driving environment. A Naive Bayes learning classifier is applied for the cognitive nature of the presented functionality. Three scenarios, with regards to drivers with different personal preferences and to driving scenes with changing environment situations, are illustrated, showcasing the effectiveness of the proposed functionality.
... Most of them are connected to peoples' missing trust in AVs [11,12], which is often connected to the inherent fundamental role shift from active drivers to passive passengers and the associated loss of control [13]. These challenges are currently addressed through user-centred design principles for AVs, especially their driving styles, e.g., [14,15]; internal, e.g., [16,17]; and external Human-Machine Interfaces, e.g., [18,19]; which aim at promoting transportation mode changes towards AVs in general. ...
When it comes to climate change, automated vehicles (AV) are often presented as a key factor to reducing emissions related with the transport sector. While studies promise emissions savings of up to 80%, it is often overlooked how AVs will be introduced and which transportation mode changes will arise from their implementation. Therefore, this online survey examined usage intentions regarding private and shared AV types, and underlying attitudes and mobility needs of 136 current users of different main modes of transport. Two main results counteract the general assumption of ecological sustainability benefits of AVs: First, current car drivers prefer private over shared AV types, even though notable sustainability gains can only be expected from shared AVs. Second, current users of
more sustainable modes of transport (walking, bike, public transport) would replace theses modes by AVs for substantial shares of their trips, which represents a behavioural rebound effect, since AVs
cannot be more sustainable than walking or biking. Group-specific mobility needs and knowledge gaps regarding the sustainability of different AV types are identified as reasons for these results and
as starting points for deriving necessary measures accompanying the introduction of AVs into society through motivating ecologically sustainable transportation mode changes.
... However, some signifiers used can be designed but are currently not considered in the design process or at least not to the extent that may be required. An obvious example is the AV's driving behaviour, where the focus of design and investigation has often been on comfort (Bellem, Schönenberg, Krems, & Schrauf, 2016;Bellem, Thiel, Schrauf, & Krems, 2018;Hartwich, Beggiato, & Krems, 2018). However, the AV's driving behaviour has not to the same degree been regarded as an information channel that influences how users make sense of the AV, which was evident in the empirical studies. ...
Automation has for a long time been embraced by the vehicle industry and in
recent years, the amount and sophistication of automation in vehicles have rapidly
increased, creating more advanced automated vehicle (AV) systems. The entry of
automation into vehicles also creates new dynamics in human-vehicle interaction,
introducing new complexities when the human and automation need to cooperate
to accomplish the driving task. Previous research has identified the importance of
user understanding of Automated Vehicles, as this affects usage directly as well as
indirectly by impacting trust and acceptance.
In this thesis, a human-centred design perspective has been chosen that uses a
product semantic framework as the basis for addressing the issue of user understanding
with the aim of exploring how users make sense of the AV during use. The research
presented is based on data from three empirical user studies conducted with users
of a (i) seemingly fully automated vehicle, (ii) vehicle with two different levels of
automation, and (iii) an automated driving system for docking buses.
The findings indicate that use of the AVs gave rise to several levels of meaning,
based on a two-part process. One was an intermeaning process, where integration of
the participants’ conceptual models, artefactual signifiers and situational signifiers
in a context developed meaning. However, an intrameaning process was also evident
where meanings themselves developed new meanings. The findings also show that
usage of the AV itself is an integral part of the process of making sense, where both
processes affect how the system is used and the usage prompts new meaning to
arise. This thesis presents a model based on the findings, describing four important
factors: the user’s conceptual model, the signifiers, the meanings that arise during
use of the AV, and the context in which it is used. The model illustrates the complex
interplay between these four components and can be used to better understand and
investigate how users make sense of AVs to aid the design and development of AVs.
The thesis also contributes to the field of product semantics through the practical
application of product semantic theories, in addition to providing further insight into
how users develop meaning and make sense of artefacts, by describing the processes
and components which seem to be the foundation when making sense of artefacts.
Having said that, further studies need to explore in greater detail the dynamics
of the process of making sense, how meaning changes during prolonged usage, and
how the model could be advanced to be able to be used in AV development and
evaluation processes.
... Other researchers have quantified driver drowsiness during automated driving using measures such as eyelid closure and posture (e.g., Hecht et al., 2019;Schömig et al., 2015). Several studies have also used a handheld control unit to continuously poll drivers' discomfort with the AV's driving style in a driving simulator (Charlton et al., 2014;Hartwich et al., 2018;Niermann et al., 2021;Radhakrishnan et al., 2020;Rossner & Bullinger, 2020). ...
Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing study with 1378 participants, who rated the perceived risk of 100 randomly selected dashcam images on German roads. The population-level perceived risk was found to be statistically reliable, with a split-half reliability of 0.98. We used linear regression analysis to predict (r = 0.62) perceived risk from two features obtained with the YOLOv4 computer vision algorithm: the number of people in the scene and the mean size of the bounding boxes surrounding other road users. When the ego-vehicle's speed was added as a predictor variable, the prediction strength increased to r = 0.75. Interestingly, the sign of the speed prediction was negative, indicating that a higher vehicle speed was associated with a lower perceived risk. This finding aligns with the principle of self-explaining roads. Our results suggest that computer-vision features and vehicle speed contribute to an accurate prediction of population subjective risk, outperforming the ratings provided by individual participants (mean r = 0.41). These findings may have implications for AV development and the modeling of psychological constructs in traffic psychology.
... Overall, the subjective sensation of comfort makes its evaluation complicated with external factors becoming important. Regarding comfort, age is a notable condition [6] and personality or personal driving preferences make each driver have different assessments for the same situations [7]. In [8], other agents that affect motion sickness are summarized (e.g., smell, sound). ...
Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms in order to identify driver actions that negatively affect passenger motion sickness. The obtained clustering models identify distinct driving patterns and associate them with the motion sickness levels suffered by the passenger, allowing a comfort-based driving recommendation system that reduces it. The designed and validated methodology shows satisfactory results, achieving (from a real datasheet) trained models that identify diverse interpretable clusters, while also shedding light on driving pattern differences. Therefore, a recommendation system to improve passenger motion sickness is proposed.
... On the other hand, many researchers in the area of human factors mainly use simulators in the laboratory arena or online surveys to examine how drivers [25], [41], [42], [43], pedestrians [44], [45] or passengers [37], [46], [47] respond to ACs, which have been programmed and designed deliberately to perform in a human-like manner, i.e., validating algorithms of ACs from the perspective of human factors studies. However, sparse research offers a true-to-life ride experience for passengers to examine the human likeness of the AC. ...
Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger's seat perspective to examine the human likeness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers' judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers' ascription of humanness, which might become a future direction for autonomous driving.
... A future study could be conducted to investigate whether eHMI signals produced by an AV (see also Lee et al., 2022) have the same effect on Malaysian pedestrians. Designing human-like AVs might also result in positive subjective feedback (Butakov & Ioannou, 2015;Hartwich et al., 2018). Although our study was only about predicting the intention of other road users rather than directly measuring participants' driving behaviour during the interactions, UK drivers' judgments seemed to reflect a more cautious approach (they were more likely to judge a vehicle as turning). ...
This study explored whether British and Malaysian drivers differ in their use of explicit (turn signals) and implicit (e.g., vehicle position, speed) communicative cues when judging the intention of other road users. Participants viewed videoclips of car drivers and motorcyclists who either continued straight or turned into a junction. The clips terminated immediately prior to any manoeuvre being made and participants were asked to judge whether or not the vehicle would turn. Explicit signals (turn indicators) were manipulated such that valid signals were made 50% of the time. Although both groups of drivers were more accurate on validly signalled trials, British drivers were more affected by signal validity, performing particularly poorly on invalid trials. British drivers were better at judging intentions of cars than motorcycles, whereas Malaysians performed better for motorcycles than cars on invalid trials. We conclude that British drivers heavily rely on explicit signals when judging intention whereas Malaysian drivers are more attuned to implicit signals. Familiarity with vehicle type may also impact performance, especially where cues are ambiguous. Implications for driving abroad and autonomous vehicles are discussed.
... Drivers prefer to select different types or levels of automation in different driving scenarios to achieve better performance [23], [25]. In conclusion, drivers expect automated driving systems to operate as they do [9] [26] [27], although they tend to evaluate their own driving styles as being aggressive [26]. As individual drivers have different driving styles [28,29], drivers with an aggressive driving style may prefer a relatively more aggressive automated driving system. ...
Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From the perspective of human factors, an automated system with different styles would improve user acceptance, because drivers could drive with different styles in different driving scenarios. This paper proposes a method to design different lane-change styles for automated driving by analyzing and modeling truck-driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver-model parameters. The lane change styles were classified into three types: aggressive, medium, and conservative. The proposed automated lane-change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effects of different lane-change decision-making styles on the driver experience and acceptance were evaluated from the perspectives of both the ego truck and surrounding vehicles. The evaluation results demonstrate that different lane-change decision-making styles can be distinguished by drivers. Overall, the three styles were evaluated by the human drivers as being safe and reliable. The main contribution of this study is that it provides the insights into the design of an automated driving system with different driving styles. Furthermore, these observations can be applied to commercial automated trucks.
... In addition, older drivers are with higher THAV, which is consistent with previous findings [26] but contrary to Park's research [18]. This phenomenon could be explained by the different age distribution of the respondents. ...
Highly automated vehicles are expected to become commonplace shortly. Driving authority is switched between the automated driving system and the human driver for highly automated vehicles. The appropriate level of drivers' trust in highly automated vehicles (THAV) plays an essential role in the safety of the switching process. Hence, the assessment of THAV and the investigation of its influencing factors are necessary for highly automated vehicles. In this paper, a second-order measurement model for THAV was established based on exploratory factor analysis and confirmatory factor analysis. Then, the affecting factors of THAV were systematically explored based on structural equation modeling. The results indicated that the proposed measurement model could effectively measure THAV. In addition, education, age, and driving experience had significant effects on THAV, while gender and accident experience showed insignificant effects on THAV. This study contributes to a systematic understanding of drivers' trust in highly automated vehicles, the development of human-centered automated driving systems, and enhancing the acceptance of highly automated vehicles.
... Other researchers have quantified driver drowsiness during automated driving using measures such as eyelid closure and posture (e.g., Hecht et al., 2019;Schömig et al., 2015). Several studies have also used a handheld control unit to continuously poll drivers' discomfort with the AV's driving style in a driving simulator (Charlton et al., 2014;Hartwich et al., 2018;Niermann et al., 2021;Radhakrishnan et al., 2020;Rossner & Bullinger, 2020). ...
Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing study with 1378 participants, who rated the perceived risk of 100 randomly selected dashcam images on German roads. The population-level perceived risk was found to be statistically reliable, with a split-half reliability of 0.98. We used linear regression analysis to predict (r = 0.62) perceived risk from two features obtained with the YOLOv4 computer vision algorithm: the number of people in the scene and the mean size of the bounding boxes surrounding other road users. When the ego-vehicle’s speed was added as a predictor variable, the prediction strength increased to r = 0.75. Interestingly, the sign of the speed prediction was negative, indicating that a higher vehicle speed was associated with a lower perceived risk. This finding aligns with the principle of self-explaining roads. Our results suggest that computer-vision features and vehicle speed contribute to an accurate prediction of population subjective risk, outperforming the ratings provided by individual participants (mean r = 0.41). These findings may have implications for AV development and the modeling of psychological constructs in traffic psychology.
... Perceived feeling of control is expected to be lower [32] and perceived safety is expected to be higher [33] in automated driving compared to manual driving. This correlates with trust in its functionality [34]. In addition to safety, perceived efficiency can also influence DX. ...
The SAE International plays a major role in shaping research and development in the field of automated driving through its SAE J3016 automation taxonomy. Although the taxonomy contributed significantly to classification and development of automated driving, it has certain limitations. SAE J3016 implies an “all or nothing” approach for the human operation of the driving task. Within this paper, we describe the potential of moving considerations regarding automated driving beyond the SAE J3016. To this end, we have taken a structured look at the system consisting of the human driver and the automated vehicle. This paper presents an abstraction hierarchy based on a literature review. The focus lies particularly on the functional purpose of the system under consideration. In particular, optional parts of the functional purpose like driver satisfaction are introduced as a main part of the target function. We extend the classification into optional and mandatory aspects to the lower levels of abstraction within the developed hierarchy. Especially the decisions on movement and dynamics in terms of driving parameters and driving maneuvers offer a so far underestimated design space for (optional) driver interventions. This paper reveals that the SAE J3016 lacks a consideration of these kind of interventions. The identified design space does not replace the SAE J3016, it does however broaden the perspective provided by this important taxonomy.
... Overall, although there are a few exceptions (e.g., Brookhuis and de Waard 2006;Xu et al. 2021), investigations into professional drivers' usage of ADAS are scarce. Earlier research has focused on private car drivers and their acceptance of ADAS (e.g., Rahman et al. 2017) or their experience of using ADAS (Adell 2009;Hartwich et al. 2018;Jun et al. 2019;Novakazi et al. 2020). ...
Due to the argued benefits of passenger comfort, cost savings, and road safety, the bus sector is showing increasing interest in advanced driver-assistance systems (ADAS). Despite this growth of interest in ADAS and the fact that work tasks are sometimes complicated (especially docking at bus-stops which may occur several hundred times per shift), there has been little research into ADAS in buses. Therefore, the aim of this study was to develop further knowledge of how professional bus drivers experience and accept an ADAS which can help them dock at bus-stops. The study was conducted on a public route in an industrial area with five different bus-stops. Ten professional bus drivers got to use a narrow navigation system (NNS) that could dock automatically at bus-stops. The participants’ experience and acceptance were investigated using objective as well as subjective data (during and after the test-drive) and data were collected using interviews, questionnaires, and video recordings. The participants indicated high levels of trust in and acceptance of the NNS and felt that it had multiple benefits in terms of cognitive and physical ergonomics, safety, and comfort. However, the relatively slow docking process (which was deemed comfortable) was also expected to negatively affect, e.g., timetabling, possibly resulting in high stress levels. Therefore, when investigating users’ acceptance of ADAS in a work context, it is important to consider acceptance in terms of the operation, use, and work system levels and how those levels interact and affect each other.
... Driving styles can be described as a stable aspect of behavior that is shown while driving and varies between individuals. The concept comprises individual preferences for speeds, headway distances, or headway times (for an overview see Sagberg et al. 2015) and should also be considered in AVs' driving styles to support the users' acceptance of automated driving functions (Hartwich et al. 2018). ...
To support road safety and user acceptance, the interaction capabilities of automated vehicles (AVs) need to be intuitive and transparent. Therefore, established interaction capabilities of manual drivers need to be implemented in AVs. In manual driving, accepted time gaps (gap acceptance, GA) are frequently applied to coordinate interactions between traffic participants. Various driver characteristics, such as age, were shown to influence GA. However, little research considered the influence of driver personality traits on GA. Therefore, the current online study investigated the effect of drivers’ sensation seeking and big five personality traits (i.e., agreeableness, extraversion, conscientiousness, openness, and neuroticism) on GA. The applied video material displayed an intersection scenario with approaching interaction partners encountering from the left of the drivers’ perspective. A total of 121 participants contributed to the study. The findings showed a significant effect for participants’ sensation seeking on GA. Participants scoring higher in sensation seeking accepted smaller time gaps resulting in riskier decisions for the turning maneuvers than participants scoring lower in sensation seeking. Moreover, the results revealed a significant difference in GA regarding participants’ agreeableness. Participants scoring higher in agreeableness indicated larger time gaps to initiate turning maneuvers (i.e., more cooperative interactions) than participants scoring lower in agreeableness. There was no effect for extraversion, conscientiousness, openness, and neuroticism on GA. To support the user acceptance of automated driving functions, differences in driving style preferences related to personal characteristics should be considered in AVs (e.g., by offering selectable driving style profiles).
... Advanced driver assistance systems (ADAS) and autonomous driving technologies are continuously developing. To successfully apply these technologies, attention should be paid to vehicle comfort, driver acceptance, and trust, on the basis of ensuring safe driving ( [20]; Nordhoff and Happee, 2016). If the ADAS controls the vehicle differently from the drivers' preferred mode of operation, the drivers may conflict with the system, which will not only be uncomfortable for the drivers [8,25], but may also lead to other unpredictable risks [28]. ...
Ensuring human‐like driving performance is fundamental to the widespread adoption and acceptance of intelligent driving systems. The speed behaviour of right‐turn drivers at signalized intersections was studied to improve the human‐like driving capability. The study was conducted using natural driving data and 545 data for drivers turning right at signalized intersections were extracted. YOLOv4 was used to identify the different types of road users. A one‐way ANOVA was used to study the influence of the scene complexity, number of lanes, and intersection shape on the speed selection behaviour. Linear regression and binary logistic regression analyses were used to study the influence of road users, number of lanes, intersection shape, and vehicle motion state on the speed control behaviour. The main results indicate the following: (1) Drivers drove slower when there were more traffic participants or fewer lanes. (2) The intersection shape did not have any effect on the speed behaviour of right‐turn drivers, either speed selection behaviour or speed control behaviour. (3) The reaction distance was affected only by the approach speed in this study; the greater the speed, the longer the reaction distance. (4) In addition to the vehicle motion state, external environmental factors also play a critical role in the braking characteristics, that is, pedestrians, cyclists, lateral traffic, and number of lanes after the turn. (5) The driver's intention to brake after entering the intersection stemmed from road users who were in collision conflict with the host vehicle and fewer lanes after the turn, rather than the speed at the stop line. This research has a guiding significance for the design of human‐like driving of right‐turn driving assistance systems. The target classification method and the measurement method of the driving environment complexity also help improve the human‐like perception of the right‐turn driving assistance system.
... Autonomous driving may be safer, energy-efficient, and more environmentally friendly than human driving. [32] There are also differences in driving habits between the young and the old [33]. ...
To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system.
... There is correlation existed between comfort and user acceptance in highly automated driving [20]. Therefore, it is crucial to provide a comfortable driving experience to ensure the acceptance of the autonomous vehicle [21,22]. The transfer of control from a human driver to an autonomous system brings negative effects toward the path naturality which resembling human generated paths [12]. ...
Motion sickness (MS) is an unpleasant sensation such as headache and nausea which occurs during travelling by vehicle. Extensive studies had been carried out regarding the factor and the mitigation methods of MS, especially for the vehicle’s passengers. Nowadays, a revolution from the automotive industry resulting from the development of the autonomous vehicles. One of the key concerns in autonomous vehicle research is that its possibility to have a higher chance to contribute to MS among the occupants compared to the conventional vehicle. Hence, this paper presents reviews on the MS reduction methods, focusing on the application towards the autonomous vehicle. Considering the importance of MS reduction in improving the occupant’s comfort level, it is concluded that this issue requires more attention among autonomous vehicle researchers.KeywordsMotion sicknessAutonomous vehicleComfortUser acceptance
... Riding in CAVs with full automation takes drivers off driving tasks and allows them to choose their preferred leisure or pick-up activities unrelated to driving [88]. Moreover, CAVs offer new travel options for special groups, e.g., the older people and the disabled [68,89], providing them with a greater extent of mobility [9]. Numerous studies have found a significant relationship related to automation with consumers' attitudes to CAVs, e.g., smart homes [87] and CAVs [9]. ...
To accelerate the widespread adoption of connected and autonomous vehicles (CAVs) and take full advantage of CAVs’ transportation safety, efficiency, and pro-environment, a deep understanding of CAVs acceptance is needed. However, little is known about the combined effects of factors influencing CAVs acceptance using traditional statistical methods. We developed an integrated model to explore how various antecedent factors work together on CAVs’ acceptance. The symmetric (Structure Equation Modeling) and asymmetric (Qualitative Comparative Analysis) techniques were utilized for analyzing data from 362 Chinese. PLS-SEM assesses the net effect of each antecedent on CAVs’ adoption, while fsQCA provides supplementary analysis by revealing the configurations of causal conditions associated with CAVs’ adoption. PLS-SEM results show that perceived usefulness, perceived ease of use, and initial trust directly influence users’ willingness to adopt CAVs, while perceived risk, social influence, and facilitating conditions do not. Meanwhile, automation, ubiquitous connectivity, structural assurance, and corporation reputation indirectly influence CAVs adoption, while environmental performance and technological uncertainty have no statistically significant indirect effect. Interestingly, fsQCA reveals five configurations resulting in a high level of CAVs’ acceptance, and seven configurations leading to the negation of CAVs’ acceptance. The complementary analysis results provide insights into both theory and practice.
... In the context of automated driving, risk perception is an understudied research topic (Brell et al., 2019). Numerous research papers have addressed similar constructs, including passenger comfort (e.g., Bellem et al., 2016Bellem et al., , 2018Hartwich et al., 2018) and trust (e.g., Mühl et al., 2020;Strauch et al., 2019) during automated driving, focusing mainly on highways and rural driving environments. However, passenger preferences for highly automated driving behavior in urban driving environments have been largely neglected. ...
The success of highly automated vehicles (HAVs; SAE Level 4) will depend to a large extent on how well they are accepted by their future passengers. This is especially true for the interaction of these vehicles with other human road users in mixed traffic. In future urban traffic, passengers of HAVs will observe from a passive position how the automated system resolves space-sharing conflicts with crossing vulnerable road users (VRUs; e.g., pedestrians and cyclists) at junctions. For one such crossing-paths conflict, we investigated when passengers would want the HAV to start braking and how much perceived risk passengers accept in the interaction of their vehicle with VRUs. To this end, we conducted 1) an online video study (N = 118), 2) a driving simulator study (N = 28), and 3) a human&vehicle-in-the-loop (Hu&ViL) study at a test site (N = 10). We varied the speed of the HAV (30 km/h vs. 50 km/h), the type (cyclist vs. pedestrian), and crossing direction of the VRU (left vs. right). During the approach to the junction, passengers' task was to trigger the HAV's braking maneuver, in a first trial at the point they considered ideal and in a second trial at the last point they still considered safe enough to decelerate and come to a stop at the stop line. For each braking maneuver, we analyzed the HAV’s distance and time-to-arrival (TTA) to the VRU at braking onset, as well as passengers’ perceived risk in the VRU interaction. The results showed that most passengers preferred harmless interactions with VRUs (at the ideal braking onset time), and accepted unpleasant, but not dangerous interactions at most (at the last acceptable braking onset time). Methodologically, the results were very similar in the three different environments (online, driving simulator, real vehicle). These results clearly show that, in addition to the technical considerations of safe automated driving, passengers’ perception and evaluation of HAV driving behavior should also be taken into account to achieve a satisfying level of acceptance of these vehicles.
... Comfort is considered as a main driver for higher level automated driving, next to efficiency, safety, accessibility and social inclusion (ERTRAC, 2019). In automated driving, new comfort aspects become important such as motion sickness, trust in the system, apparent safety, controllability, familiarity of vehicle operations as well as information about system states and actions (Domeyer et al., 2019;Beggiato, 2015;Hartwich et al., 2018). Thus, to explore the expected benefits of automated driving, acceptance needs to be ensured by avoiding unpleasant experiences of discomfort related to these new aspects. ...
Face tracking as innovative and unobtrusive sensor technology offers new possibilities for driver state monitoring regarding discomfort in automated driving. To explore the potential of automated facial expression analysis, video data of two driving simulator studies were analyzed using the Visage facial features and analysis software. A gender-balanced sample of 81 participants between 24 and 84 years took part in the studies. All participants were driven in highly automated mode on the same standardized track, consisting of three close approach situations to a truck driving ahead. By pressing the lever of a handset control, all participants could report perceived discomfort continuously. Tracking values for 23 facial action units were extracted from multiple video camera streams, z-transformed and averaged from 10 s before pressing the handset control until 10 s after to show changes over time. Results showed situation-related pressing and stretching of the lips, a pushback-movement of the head, raising of inner brows and upper lids as well as reduced eye closure. These patterns could be interpreted as visual attention, tension and surprise. Overall, there is potential of facial expression analysis for contributing information about users’ comfort with automated vehicle operations. However, effects became manifest on aggregated data level; obtaining stable and reliable results on individual level remains a challenging task.
... Hartwitch et al. (2018) stressed that such results tend to be obtained without participants having had the opportunity to experience an automated driving situation. Notwithstanding, research has emphasized the need to include the pleasure of manual driving when addressing the acceptability of future AVs (Hartwich et al. 2018, Payre et al. 2014). ...
Addressing the acceptability of automated vehicles (AVs) implies, beyond technical, legal, or ethical aspects, the debate on perceptions and use intentions. The focus of this study is placed on questioning the technique by the social dimension: what acceptability profiles emerge from these perceptions? This study analyzes the determinant factors of AVs acceptability to identify different Portuguese population clusters. A survey was developed, in the scope of the AUTODRIVING project, with 501 participants. Three acceptability clusters were identified: Objectors; Ambivalent; and Enthusiasts. To complement these results, five focus groups were carried out, involving both professional and regular drivers. The results enabled the access to a situated point of view, considering the current experience of driving, particularly in the case of professional drivers. This study could contribute to deploying AVs, highlighting the importance of a contextualized analysis hic and nunc, and allowing to bring to the fore the demands and constraints of driving.
... Comfort is considered as a main driver for higher level automated driving, next to efficiency, safety, accessibility and social inclusion (ERTRAC, 2019). In automated driving, new comfort aspects become important such as motion sickness, trust in the system, apparent safety, controllability, familiarity of vehicle operations as well as information about system states and actions (Domeyer et al., 2019;Beggiato, 2015;Hartwich et al., 2018). Thus, to explore the expected benefits of automated driving, acceptance needs to be ensured by avoiding unpleasant experiences of discomfort related to these new aspects. ...
Psychoactive substances are natural or synthetic compounds that affect the central nervous system by depressing, stimulating or producing hallucinations, generating brain´s impairments, specifically in those mechanisms that regulate mood, thoughts, and motivations. These substances usage and dependency represent a significant fact upon mortality rates worldwide, making necessary the creation of a conceptual proposal about a mobile application for the treatment of addictions to psychoactive substances, which, at the same time, is concordant with the addiction centered therapeutical methodology, conducted by mental health professionals, highlighting the necessity of psychiatric and psychological support, plus a technological advance. It should be noted that this is a fictitious exploratory study that provides guidelines for future development.
... Driver's control of the brake, the throttle and the steering wheel results in low-frequency/high-magnitude disturbances that induce certain accelerations in the longitudinal and lateral directions with respect to a front facing passenger [24], and hence, driving style affects also passenger comfort. However, few works evaluate these activities from ride comfort perspective, among them [25][26][27][28] can be cited. ...
Advanced driving assistance systems (ADAS) are primarily designed to increase driving safety and reduce traffic congestion without paying too much attention to passenger comfort or motion sickness. However, in view of autonomous cars, and taking into account that the lack of comfort and motion sickness increase in passengers, analysis from a comfort perspective is essential in the future car investigation. The aim of this work is to study in detail how passenger’s comfort evaluation parameters vary depending on the driving style, car or road. The database used has been developed by compiling the accelerations suffered by passengers when three drivers cruise two different vehicles on different types of routes. In order to evaluate both comfort and motion sickness, first, the numerical values of the main comfort evaluation variables reported in the literature have been analyzed. Moreover, a complementary statistical analysis of probability density and a power spectral analysis are performed. Finally, quantitative results are compared with passenger qualitative feedback. The results show the high dependence of comfort evaluation variables’ value with the road type. In addition, it has been demonstrated that the driving style and vehicle dynamics amplify or attenuate those values. Additionally, it has been demonstrated that contributions from longitudinal and lateral accelerations have a much greater effect in the lack of comfort than vertical ones. Finally, based on the concrete results obtained, a new experimental campaign is proposed.
... Fig. 1: The instrumented car used in this researchjective and providing only retrospective information on the passenger's stress. Only few techniques for real-time discomfort acquisition have been proposed in the literature, namely, a handset controller[10] [6] and the use of physiological sensors[11] [12][13] [15][16]. ...
The I.DRIVE Laboratory of Politecnico di Milano, aims at developing inter-disciplinary proficiency required for the analysis and modeling of behavioral aspects due to the interaction between driver, vehicle, infrastructure, and environment. The present research outlines the development of a software and hardware platform that allows studying, in a real car, the interaction between driver, car, road infrastructure, and environment. In particular, we focus on driver stress and we want to understand which are the driving factors that impact driver stress in order to ease future wide acceptance of car automation. To validate our framework, six drivers performed a manual drive, an autonomous drive, and a manual drive as passengers in a controlled scenario. Results show that longitudinal jerk, angular velocity on x-axes, and lateral acceleration have a high correlation with two stress indexes, the skin conductance’s phasic component, and the respiration rate.KeywordsAutonomous carDriver stressHuman-robot interaction
... In automobile-dependent societies such as Australia, use of these technologies could result in improved access to services, greater social participation, and may improve the ability of older people to continue to be independent with community-based occupations (Piau et al., 2014). Furthermore, the use of automated driving technologies could improve driver safety, enjoyment, and comfort when driving (Classen et al., 2019;Hartwich et al., 2018). ...
When older adults’ driving abilities decline, automated driving technologies may improve community mobility, engagement, and independence. Most previous research has focused on older persons’ attitudes rather than their use of automated driving technologies. This study examined older Australians’ perceptions and experience of automated vehicle technologies before, during, and after a real-life driving experience, focusing on ease of use, usefulness, safety, acceptance, trust, and confidence. This mixed-methods study included observation of a 6-km test drive using a partially automated vehicle, pre- and post-drive questionnaires, and a post-drive semi-structured interview. Most participants reported positive perceptions and experiences before, during, and after the test drive. Visual analysis of pre/postresponses revealed divergent reactions to the test drive, consistent with the heterogeneity of the older population. Automated driving technologies have potential to contribute to mobility at older ages. Larger-scale studies including actual driving experiences are recommended.
Under the human-automation codriving future, dynamic trust should be considered. This paper explored how trust changes over time and how multiple factors (time, trust propensity, neuroticism, and takeover warning design) calibrate trust together. We launched two driving simulator experiments to measure drivers' trust before, during, and after the experiment under the takeover scenario. The results showed that trust in automation increased during short-term interactions and dropped after four months, which is still higher than pre-experiment trust. Initial trust and trust propensity had a stable impact on trust. Drivers trusted the system more with the two-stage (MR + TOR) warning design than the one-stage (TOR). Neuroticism had a significant effect on the countdown compared with the content warning.
In autonomous vehicles (AVs), especially in fully AVs, “drivers” perceive vehicle operation from a passenger’s perspective. This study focuses on the perspective change of drivers especially in fully AVs. To investigate the effect of change in perspective on drivers’ assessment of AVs, this study conducted a driving simulator experiment and a survey using different samples. The driving simulator experiment was a within-subjects design including 40 participants. The experimental results indicated that although AVs drive exactly the same as the drivers, 85% of these drivers had different assessments for AVs compared to driving themselves (self-AV bias). Among these biased drivers, 80% changed the direction of their assessments (e.g., satisfied with their own driving behaviors but dissatisfied with the same behaviors from the AVs). Additionally, self-AV bias increased with the level of self-evaluation of their own driving skills. The higher the level of self-evaluation, the higher the optimism bias (drivers think they drive better than AVs) of drivers. Thereafter, to show whether and how self-AV bias affects drivers’ intuitive acceptance of AVs, a survey was conducted, which included 381 valid questionnaires. The survey results showed that self-AV bias was a critical factor promoting perceived usefulness of AVs; particularly, highly self-AV-biased drivers responded more to the perceived usefulness of AVs. Moreover, drivers who could accept more risky driving behaviors of AVs compared to themselves also perceived AVs to be more useful. The findings from this study provide insights for understanding drivers’ assessment and acceptance of AVs’ driving behaviors.
The foggy freeway is a scenario with a high incidence of accidents. Different traffic flow conditions will affect the driving behavior of conditionally autonomous vehicle drivers in the foggy areas of the freeway. In this study, the experiment was carried out by using the driving simulation system. Forty-two participants drove on a foggy freeway according to two traffic conditions and two non-driving related tasks. Wilcoxon Signed-Rank Test and Survival Analysis were applied to explore the impact. The results show that the effects of different non-driving related tasks on driving behavior are mainly concentrated in the takeover process; The deceleration reaction time of the free flow (6.04s) is much lower than that of bound flow (9.40s); In the condition of bound flow, the driver is more inclined to slow down without braking. Potential application areas of this research comprise safety assessment of conditional autonomous driving and formulating autonomous driving policy.
Although existing research identified influences of age and gender on Automated Vehicle (AV) acceptance, the underlying reasons were not revealed. A potential reason is that age and gender are exogenous variables, which do not change by other variables. There must exist endogenous variables, such as the built environment and personal factors, such as affordability, travel needs, exposure to AV knowledge, acting as mediating factors that bridge the exogenous variables and AV acceptance. However, these mediating effects have not been discovered, validated, and quantified. Therefore, this paper provides a new viewpoint in unveiling how ages and genders influence acceptance of AV by quantitatively revealing hidden mediating effects focusing on the built environment and personal factors. A statewide survey was conducted in Kentucky. Besides demographical information, respondents’ personal information such as travel needs, affordability, exposure to AV knowledge, and the built environment were collected. Results reveal that males with high levels of travel needs and affordability better accept AV due to higher familiarity and more experience riding AV. Younger adults are more likely to have higher AV acceptance levels than older adults because younger adults tend to live in an urban setting with higher exposure levels to AV technology. Results suggest that experience in riding an AV, the most influential factor, improves acceptance by 44.8%. The research informs transportation agencies of a better understanding of how people of different ages and genders accept AV.
La perception du risque est un aspect crucial qu'il est nécessaire d'étudier dans le but de favoriser l'acceptation des véhicules autonomes dans des espaces partagés avec des piétons. Ce travail s'articule autour de trois contributions empiriques. La première contribution porte sur la possibilité de modéliser la dynamique de perception subjective du risque par le biais d'une réponse impulsionnelle Les performances obtenues sont assez inégales et reflètent une grande variabilité interindividuelle dans la stratégie d'évaluation du risque. Les deux autres contributions mettent en évidence que la perception du risque peut se mesurer en temps réel et dans des interactions dynamiques complexes en s'appuyant à la fois sur des mesures subjectives et des mesures de réactions électrodermales.Les résultats de ces contributions sont basés sur une modélisation par réseau bayésien permettant d'analyser simultanément les relations entre facteurs dynamiques et mesures de perception du risque. Les conclusions de ces travaux apportent des éléments théoriques sur les liens entre les dimensions implicite et explicite du risque ressenti, ainsi que des éléments concrets permettant de prendre en compte le facteur humain dans le développement des comportements de navigation des véhicules autonomes.
Achieving optimal performance in human-machine systems, such as highly automated vehicles, relies, in part, on individuals’ acceptance and use of the system, which is in turn affected by their enjoyment of engaging with, or experiencing, the system. This driving simulator study investigated individuals’ real-time subjective evaluation of four different Automated Vehicle (AV) driving styles, in different environmental contexts. Twenty-four participants were recruited to manually drive a contextually rich simulator environment, and to experience human-like and non-human-like AV driving styles, as well as the automated replay of their own manual drive. Their subjective real-time feedback towards these driving styles was analyzed. Our results showed that participants gave higher positive feedback towards the replay of their own drive, compared to the other three controllers. This difference was statistically significant, when compared to the high-speed controller (named as Fast), particularly for sharp curves. With respect to the replay of their own drive, participants gave higher negative feedback when navigating an Urban environment, compared to Rural settings. Moreover, changes in roadside furniture affected individuals’ feedback, and this effect was more prominent when the vehicle was driving closer to the edge of the road. Based on our results, we conclude that individuals’ perception of different AV driving styles changes based on different environmental conditions, including, but not limited to, road geometry and roadside furniture. These findings suggest that humans prefer a slower human-like driving style for AV controllers that adapts its speed and lateral offset to roadside objects and furniture. Investigating individual differences in AV driving style preference showed that low Sensation Seeking individuals preferred the slower human-like controller more than the faster human-like controller. Consideration of this human-centered feedback is important for the design of future AV controllers, to enhance individuals’ ride experience, and potentially improve acceptance and use of these vehicles.
Lane-changing is a critical issue for autonomous vehicles (AVs), especially in complex environments. In addition, different drivers have different handling preferences. How to provide personalized maneuvers for individual drivers to increase their trust is another issue for AVs. Therefore, a framework of human-like path planning is proposed in this paper, considering driver characteristics of visual-preview, subjective risk perception, and degree of aggressiveness. In the decision making module, a model is built to select the most suitable merging spot, with respect to safety factors and the driver’s degree of aggressiveness. And a novel environmental potential field (PF) suitable for arbitrary road structures is designed to describe the driver’s individual risk perception. In the trajectory planning module, a model predictive control (MPC) based path planner is designed according to the decisions in coincidence with the driver’s individual intentions of collision avoidance. Simulation results have demonstrated that the proposed path planner can provide with personalized trajectories for different combinations of driver preferences and steering characteristics, in scenarios of curved roads with different risks of collision.
This study investigated the effect of age on driving behavior and provided a neurophysiological interpretation. Two age group of participants have driven on a 10-mile interstate highway on a driving simulator under different traffic density and driving mode. Driving performance, eye movement, brain activity, and subjective workload were measured. Results showed that age didn’t affect the driving performance or brain activity. But the subjective workload and eye movement were significantly different among the two age groups. Moreover, drivers’ subjective workload was not consistent with the eye movement. The study should provide insights to future studies about the effect of human factors in driving behavior.
The purpose of the study was to develop a user requirement model aiming to obtain the critical attitudes of drivers to a driver-vehicle interaction system in China. An online survey with a questionnaire based on the proposed model was conducted. Six clusters were identified through Exploratory Factor Analysis, and five clusters showed high reliability and validity. The result of the survey indicated that the designed model was usable, and the current study provided a new model of user requirements for designing driver-vehicle interaction. This finding gives insights into developing a guideline for automobile manufacturers to user requirements’ survey.
Prior studies of automated driving have focused on drivers’ evaluations of advanced driving assistance systems and their knowledge of the technology. An on-road experiment with novice drivers who had never used automated systems was conducted to examine the effects of the automation on the driving experience. Participants drove a Tesla Model 3 sedan with level 2 automation engaged or not engaged on a 4-lane interstate freeway. They reported that driving was more enjoyable and less stressful during automated driving than manual driving. They also indicated that they were less anxious and nervous, and able to relax more with the automation. Their intentions to use and purchase automated systems in the future were correlated with the favorableness of their automated driving experiences. The positive experiences of the first-time users suggest that consumers may not need a great deal of persuading to develop an appreciation for partially automated vehicles.
Objective:
This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction.
Background:
Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers.
Method:
Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires.
Results:
Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting.
Conclusion:
Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs.
Application:
This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.
This paper presents a synthesis of existing empirical acceptance studies on automated driving and scientific literature on technology acceptance. The objective of the study was to study user acceptance of SAE Level 4 vehicles or driverless podlike vehicles without a steering wheel and pedals that operated within the constraints of dedicated infrastructure. The review indicates that previous acceptance studies on automated driving are skewed toward car users and thus create a need for targeted acceptance studies, including users of public transport. For obvious reasons, previous studies targeted respondents who had not experienced driverless vehicles. As driverless vehicles are currently being demonstrated in pilot projects, their acceptance by users inside and outside such vehicles can now be investigated. Addressing the multidimensional nature of acceptance, a conceptual model that integrates a holistic and comprehensive set of variables to explain, predict, and improve user acceptance of driverless vehicles was developed. The model linked two dominant models from the technology acceptance management literature, the unified theory of acceptance and use of technology and the pleasure-Arousal-dominance framework, with a number of external variables that were divided into system-specific, user, and contextual characteristics.
This chapter reports on a series of studies on driver behavior with a highly automated vehicle, conducted as part of the European project CityMobil and the UK project EASY. Using the University of Leeds Driving Simulator, a number of urban and highway scenarios were devised, where lateral and longitudinal control of the vehicle was managed by an automated controller. Drivers’ uptake of non-driving related tasks, their response to critical events, and their ability to resume control of driving, were some of the factors studied. Results showed some differences in performance based on the road environment studied, and suggest that whilst resuming control from automation was manageable when attention was dedicated to the road, diversion of attention by secondary tasks impaired performance when manual control resumed.
The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (⩾60 years) compared to younger drivers (⩽28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.
In the domain of automated driving the design of automated driving functions remains a crucial factor to achieve high user acceptance. The paper addresses this topic and focuses on the research question whether drivers prefer their own or at least a similar driving style when driving in an automated mode. Therefore we conducted two simulator studies investigating overtaking maneuvers under three situations on a two-lane highway in DLR's dynamic driving simulator. Experiment 1 was conducted to collect manual driving data. We applied the tool CONFORM (Conflict recognition by image processing methods) to cluster the data into four different driving style clusters per situation. In experiment 2 participants of experiment 1 returned and drove an automated vehicle (SAE Level 2) and named their preference among the different driving styles. Most drivers preferred their driving style or a similar one. However some drivers also preferred driving styles different to their driving style. Driving styles with a small safety margin and high acceleration were disliked by all participants.
Current technology developments and potential safety and mobility benefits of self-driving cars are discussed. It poses questions and proposes some initial actions to prepare the profession for the to become actively engaged in partnerships with a variety of stakeholders, including software and systems developers, auto manufacturers, and regulatory bodies. Self-driving cars offer the promise of allowing older citizens and those with disabilities to enjoy a level of mobility on a par with that enjoyed by licensed drivers with ready access to cars. In addition to the safety and mobility benefits, self-driving cars would allow significant productivity increases for commuting, goods movement, and care giving. When fully autonomous vehicles are permitted on the roadways, the fundamental nature of vehicle trips will change. Vehicles can shuttle empty to preposition themselves where they are needed. Self-driving vehicles are expected to track more precisely within lanes, which could allow lanes to be narrowed.
Automated driving changes the role of the driver from an active operator towards a supervisor during partially automated driving and passenger in the highly automated driving mode. To foster successful interaction between humans and automated systems, feedback on automation stages and behaviors is considered a key factor. The present study used a two-step procedure to investigate drivers' information needs during partially and highly automated driving in comparison to manual driving for highway scenarios. The first step consisted in an expert focus group on expected information needs. Results showed that independent from specific scenarios, information should provide transparency, comprehensibility, and predictability of system actions. This includes the current system status, the remaining time to a change in the level of automation, the fallback level as well as reasons and a preview for ongoing and subsequent maneuvers. In the second step, results from the expert focus group were used to set up a driving simulator study. A sample of 20 participants performed three highway trips on the same route either in the manual, partially automated (hands-on, permanent monitoring, no secondary task) as well as highly automated condition (cloze test on a laptop as secondary task). Questionnaires and interviews about information needs were applied after each trip and glance behavior was analyzed. Information needs showed great variance between the drivers, which can mainly be explained by trust in automation. Partially automated driving was considered more exhausting than the other conditions due to the continuous supervision task. Information needs for the automated conditions were primarily related to the supervision of the system, whereas requested information during manual driving was centered on performing the current driving task. Glance data supported these patterns: during partially automated driving, drivers showed most and longer control glances at the mirrors and instrument cluster. Secondary task engagement during highly automated driving varied in dependence of trust in automation and the perceived complexity of the situation. However, less salient objects in a situation, such as traffic signs, were not perceived and no control glances were performed. It can be concluded that information needs change for partially and highly automated driving. Requested information is primarily focused on the status, transparency and comprehensibility of system action in contrast to driving-task related information during manual driving. These changes need to be considered in the human-machine-interface (HMI) design for automated driving.
Der Beitrag beginnt mit einer Bestimmung dessen, was unter (Technik-)Akzeptanz zu verstehen ist, und diskutiert anschließend, welche Forschungsschwerpunkte im Zusammenhang mit autonomem Fahren relevant sind. Der empirische Teil wird mit Ergebnissen aus aktuellen Studien zur Akzeptanz des autonomen Fahrens eingeleitet, bevor anschließend Ergebnisse einer eigenen Untersuchung vorgestellt werden, die der Sicht heutiger Verkehrsteilnehmerinnen und -teilnehmer nachspürt und auf diese Weise Erkenntnisse für künftige, noch stärker anwendungsbezogene Empirie zur Akzeptanz des autonomen Fahrens gewinnt.
The paper introduces the method and tool CONFORM (Conflict regocnition by image processing methods). CONFORM will be integrated as a driver model into the ibeo test vehicle during the project phase of EU?project HoliDes. The aim of CONFORM is to support the system designer to properly parameterize the default behavior of a highly automated vehicle to guarantee a high system acceptance. Thereby CONFORM addresses intra and inter individual differences in the driving behavior. CONFORM measures the difference between the default system behavior and the natural driving behavior of a human driver situation-dependent to determine the necessity of an adaptation. Based on a driving simulator study the paper describes how CONFORM is able to visualize and to cluster certain driving patterns/styles in a vehicle following/vehicle approaching scenario. We use the study results to derive recommendations for the design of the system behavior of highly automated vehicles.
The prospect of driverless cars wide-scale deployment is imminent owing to the advances in robotics, computational power, communications, and sensor technologies. This promises highway fatality reductions and improvements in traffic and fuel efficiency. Our understanding of the effects arising from commuting in autonomous cars is still limited. The novel concept of the loss of driver controllability is introduced here. It requires a reassessment of vehicle's comfort criteria. In this review paper, traditional comfort measures are examined and autonomous passenger awareness factors are proposed. We categorize path-planning methods in light of the offered factors. The objective of the review presented in this article is to highlight the gap in path planning from a passenger comfort perspective and propose some research solutions. It is expected that this investigation will generate more research interest and bring innovative solutions into this field.
Automated automobiles will be on our roads within the next decade but the role of the driver has not yet been formerly recognised or designed. Rather, the driver is often left in a passive monitoring role until they are required to reclaim control from the vehicle. This research aimed to test the idea of driver-initiated automation, in which the automation offers decision support that can be either accepted or ignored. The test case examined a combination of lateral and longitudinal control in addition to an auto-overtake system. Despite putting the driver in control of the automated systems by enabling them to accept or ignore behavioural suggestions (e.g. overtake), there were still issues associated with increased workload and decreased trust. These issues are likely to have arisen due to the way in which the automated system has been designed. Recommendations for improvements in systems design have been made which are likely to improve trust and make the role of the driver more transparent concerning their authority over the automated system.
Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Menschen repräsentieren Wissen und Lernerfahrungen in Form von mentalen Modellen. Dieses aus der Kognitionspsychologie stammende Konzept ist eines der zentralen theoretischen Paradigmen für das Verständnis und die Gestaltung der Interaktion von Menschen mit technischen Systemen [1]. Mentale Modelle dienen in diesem Kontext einerseits der Beschreibung menschlicher Informationsverarbeitung, z. B.
One of the main concerns of automobile manufacturers is the optimization of cars conceiving. For this reason the integration of clients’ perceptions in the manufacturing process is an important aspect of product development. This paper aims to study the perception of young drivers over the elements of discomfort that occur while driving a vehicle. 40 subjects, young drivers (technical university students) participated in the study and were investigated in connection with the main elements of discomfort experienced in the car. Valuation of discomfort was based on descriptions of study participants, descriptions in which they were asked to specify what they perceive as discomfort in the car. On one hand, the results show us a representation of what does discomfort for young drivers mean, and on the other hand, it shows the important role that the thermal factor has in assessing comfort / discomfort in the vehicle.
Objective: We examined whether participants would trust an agent that was similar to them more than an agent that was dissimilar to them.
Background: Trust is an important psychological factor determining the acceptance of smart systems. Because smart systems tend to be treated like humans, and similarity has been shown to increase trust in humans, we expected that similarity would increase trust in a virtual agent.
Methods: In a driving simulator experiment, participants (N = 111) were presented with a virtual agent that was either similar to them, or not. This agent functioned as their virtual driver in a driving simulator, and trust in this agent was measured. Furthermore, we measured how trust changed with experience.
Results: Prior to experiencing the agent, the similar agent was trusted more than the dissimilar agent. This effect was mediated by perceived similarity. After experiencing the agent, the similar agent was still trusted more than the dissimilar agent.
Conclusion: Just as similarity between humans increase trust in another human, similarity also increases trust in a virtual agent. When such an agent is presented as a virtual driver in a self-driving car, it could possibly enhance the trust people have in such a car.
Application: Displaying a virtual driver that is similar to the human driver might increase trust in a self-driving car.
If previous research studied acceptability of partially or highly automated driving, few of them focused on fully automated driving (FAD), including the ability to master longitudinal control, lateral control and maneuvers. The present study analyzes a priori acceptability, attitudes, personality traits and intention to use a fully automated vehicle.
421 French drivers (153 males, M = 40.2 years, age range 19–73) answered an online questionnaire.
68.1% Of the sample a priori accepted FAD. Predictors of intention to use a fully automated car (R2 = .671) were mainly attitudes, contextual acceptability and interest in impaired driving (i.e. the two components of FAD acceptability), followed by driving related sensation seeking, finally gender.
FAD preferred use cases were on highways, in traffic congestion and for automatic parking. Furthermore, some drivers reported interest in impaired driving misuses, despite awareness of their responsibility for both the vehicle and the driving. These results are discussed regarding previous knowledge about acceptability of advanced driving assistance systems and consequences for the use of fully automated cars.
The purpose of Advanced Driver Assistance Systems (ADAS) is that driver error will be reduced or even eliminated, and efficiency in traffic and transport is enhanced. The benefits of ADAS implementations are potentially considerable because of a significant decrease in human suffering, economical cost and pollution. However, there are also potential problems to be expected, since the task of driving a ordinary motor vehicle is changing in nature, in the direction of supervising a (partly) automated moving vehicle.
Der Straßenverkehr zeichnet sich dadurch aus, dass sich Menschen mit sehr heterogenen Voraussetzungen und Bedürfnissen aktiv daran beteiligen. Im ersten Schwerpunkt Verkehrsteilnehmer und Verkehrssicherheit stellen Autoren aus verschiedenen Disziplinen ihre Forschungsergebnisse, z.B. zum Unfallrisiko von unterschiedlichen Verkehrsteilnehmergruppen und zur Wirksamkeit von Maßnahmen zur Erhöhung der Verkehrssicherheit vor. Im zweiten Schwerpunkt Beanspruchung, Situationsbewusstsein und Fahrerverhalten wird die hohe Informationsdichte durch mehr und schnelleren Verkehr und zunehmende Funktionen im Auto adressiert. Diskutiert werden geeignete Messmethoden für diese Fragestellungen. Im dritten Schwerpunkt Gebrauchstauglichkeit, Komfort und Akzeptanz werden sowohl methodische als auch produktbezogene Forschungsergebnisse dargestellt: Neben Forschungsergebnissen zu den Begrifflichkeiten und ihrer Messbarkeit werden beispielsweise konkrete Evaluationen vorgestellt.
This contributed volume covers all relevant aspects of road vehicle automation including societal impacts, legal matters, and technology innovation from the perspectives of a multitude of public and private actors. It is based on an expert workshop organized by the Transportation Research Board at Stanford University in July 2013. The target audience primarily comprises academic researchers, but the book may also be of interest to practitioners and professionals. Higher levels of road vehicle automation are considered beneficial for road safety, energy efficiency, productivity, convenience, and social inclusion. The necessary key technologies in the fields of object-recognition systems, data processing, and infrastructure communication have been consistently developed over the recent years and are mostly available on the market today. However, there is still a need for substantial research and development, e.g. with interactive maps, data processing, functional safety, and the fusion of different data sources. Driven by stakeholders in the IT industry, intensive efforts to accelerate the introduction of road vehicle automation are currently underway.
In the course of the current demographic change, the proportion of the population aged 65 and older is projected to steadily increase in many countries of the world (UN DESA Population Division, 2015). The ageing society is reflected in an increasing number of older road users (Koppel & Berecki-Gisolf, 2015), especially considering the growing need for older adults to maintain individual mobility (Eby & Molnar, 2012). This development raises new issues of transportation research, since age-related changes in mobility patterns as well as sensory, cognitive, and motor functions reduce older adults’ traffic safety (Polders, Vlahogianni, Leopold, & Durso, 2015). Accordingly, new strategies to aid older drivers and their mobility needs are required, which could potentially be provided by emerging in-vehicle technologies (Karthaus & Falkenstein, 2016). The overall aim of present dissertation project was to evaluate whether in-vehicle technologies that appear promising to support older drivers can actually contribute to their individual mobility, which requires an improvement in aspects related to driving performance as well as the acceptance of such systems in this age group. Therefore, contact-analogue head-up displays (also labelled as Augmented Reality Displays, ARDs) and highly automated driving were selected as two exemplary technologies, representing completely different levels of driving automation and accordingly different approaches to support drivers. The ARD-technology represents a technical implementation approach for IVIS and therefore an example for Automation Level 0 (no automation; SAE International, 2014) by helping the driver to execute the driving task manually through useful information. In contrast, the HAD-technology aims at supporting the driver by taking over the driving task, which corresponds to Automation Level 4 (high automation; SAE International, 2014). Despite these different approaches, both technologies were previously assumed to have a strong potential to support especially older drivers (Meyer & Deix, 2014; Polders et al., 2015; Rusch et al., 2013; Schall et al., 2013). Three empirical studies were conducted to examine performance- and acceptance-related aspects of both technologies. All studies were carried out with a group of older drivers (maximum age range: 65 85 years) and a younger comparison group (maximum age range: 25-45 years) representing the ‘average’ (i.e. young, but experienced) driver in order to identify age-specific results. Focusing on performance-related aspects of the ARD-technology, Study I represents a reaction time experiment conducted in a driving simulator. One age-specific beneficial function of such an ARD is to provide prior information about approaching complex traffic situations, which addresses older drivers’ tendency to process multiple information successively (serially) rather than simultaneously (parallel) (Davidse, Hagenzieker, van Wolffelaar, & Brouwer, 2009; Küting & Krüger, 2002). Therefore, the aim of this study was to examine the effects of an ARD providing prior information about approaching intersections on drivers’ speed and accuracy of perceiving these intersections, which is considered a necessary precondition for a safe driving performance (Crundall & Underwood, 2011). Based on concerns about the counterproductive effects of presenting information via an ARD, especially in cases of inaccurate information, system failures were included in this examination. The ARD-information aided drivers from both age groups in identifying more relevant aspects of the intersections without increasing response time, indicating the potential of the system to support both older and younger drivers in complex traffic situations. Experiencing system failures (i.e. inaccurate information) did offset this positive effect for the study’s duration, particularly for older drivers. This might be because it was difficult to ignore inaccurate prior information due to their presentation via an ARD. Study II represents a driving simulator study on acceptance-related aspects of an ARD providing prior information about approaching intersections. This study focused on the effects of system experience on drivers’ acceptance as well as on the identification of age-specific acceptance barriers that could prevent older drivers from using the technology. In summary, older and younger drivers’ evaluation of the ARD was positive, with a tendency to more positive evaluations with than without system experience in the driving simulator. Compared to the younger group, older drivers reported a more positive attitude towards using the ARD, even though they evaluated their self-efficacy in handling the system and environmental conditions facilitating its usage as less strong. Both performance- and acceptance-related aspects of HAD were addressed in Study III, a two-stage driving simulator study. The focus of the performance perspective shifted in parallel with the shift of the human role from driver to passenger due to the increasing driving automation. Accordingly, the examination of HAD was focused on the human evaluation of the automated system’s driving performance. In this context, affective components of human-automation interaction, such as comfort and enjoyment, are considered important for the acceptance and thus usage of automated vehicles (Tischler & Renner, 2007). It is assumed that the implemented driving style has an impact on such affective components in the context of HAD (Bellem, Schönenberg, Krems, & Schrauf, 2016). One theoretical approach to increase the comfort of HAD recommends the implementation of familiar, natural driving styles to mimic human control (Elbanhawi, Simic, & Jazar, 2015). Therefore, the effects of driving automation and the familiarity of the HAD-style on driving comfort and enjoyment were examined. Automation increased both age groups’ comfort, but decreased younger drivers’ enjoyment. For all dependent variables, driving style familiarity significantly interacted with drivers’ age the same way: while younger drivers preferred a familiar HAD-style, older drivers preferred an unfamiliar driving style in a highly automated context. Accordingly, the familiarity approach can be supported at least for younger drivers, but not for older drivers, whose manual driving styles are characterised by strategies to compensate for age-related impairments of sensory, cognitive, or motor functions. HAD-style preferences of this age group seem to be more influenced by the desire to regain a driving style free from these compensation strategies than by a need for familiar driving manoeuvres. In parallel with the evaluation of the ARD, acceptance-related issues in the context of HAD included the effects of system experience on drivers’ acceptance and potential age-specific acceptance barriers. Considering a system-specific design issue, it was additionally examined whether drivers’ acceptance of HAD is modifiable by the familiarity of the implemented driving style. In this driving simulator study, members of both age groups showed slightly positive a priori acceptance ratings, which significantly increased after the initial experience and remained stable afterwards. Similar to drivers’ acceptance of the ARD, older drivers reported a more positive attitude towards using HAD despite their lower self-assessed self-efficacy and environmental conditions facilitating HAD-usage compared to younger drivers. Regarding HAD-style, acceptance was subject to the same interaction between drivers’ age and driving style familiarity as driving comfort and enjoyment. These findings demonstrate that effective approaches to support the independent mobility of older adults are provided by emerging in-vehicle technologies on different levels of driving automation. The majority of the performance-related improvements did apply to both older and younger drivers, confirming that automotive technologies suggested for older drivers have the potential to support drivers of other age groups as well. Regarding drivers’ acceptance, findings suggest that both systems would be accepted by different age groups, which correspondents to the results from the performance perspective. The comparable acceptance patterns identified for two systems at different stages of driving automation, such as ARDs and HAD, indicate underlying general aspects of older adults’ acceptance of in-vehicle technologies. This includes their strong need to preserve their individual mobility as well as their lower self-efficacy in handling relevant technologies and insufficient access to a support infrastructure. These insights can enrich both theories of older drivers’ acceptance of in-vehicle technologies and measures to ensure the successful development and introduction of systems aiding them in maintaining a safe individual mobility. Considering the importance of driving for older adults’ physiological and psychological well-being (e.g. Adler & Rottunda, 2006; Lutin, Kornhauser, & Lerner-Lam, 2013), these results emphasise the potential of emerging in-vehicle technologies to improve both older drivers’ traffic safety and quality of life.
This paper addresses the issue of enabling a comfortable highly automated driving style.
Two studies have been conducted to identify metrics which can be used to parametrize
a high-quality automated driving style for automobiles with regard to safety, functionality
and comfort. The studies were set either in an urban and rural or a highway environment.
Participants (N = 12 per study) manually drove a round course assuming either an everyday,
a comfortable, or a dynamic driving style in randomized order. The obtained results
emphasize the importance of maneuver-based analysis. Namely, a variety of maneuverspecific
metrics, such as acceleration, jerk, quickness and headway distance in seconds,
were identified, which are prerequisites to differentiate between the three driving styles.
These metrics seem to be the essential components for the development of comfortable
highly automated driving.
(Full-text available thru August 24th: http://authors.elsevier.com/a/1TKBn_V9P1Fwfm)
German and European vehicle manufacturers and automotive suppliers have been at the forefront of developing and commercializing advanced driver assistance systems in the past. They are thus well prepared to proceed towards increasing levels of road vehicle automation, and engage in a multitude of technology development and demonstration actions around the world, now. Nonetheless, serious steps in reliability, security and affordability of the key enabling technologies still need to be taken and solutions for the liability issues and legal requirements have to be found before a broad rollout of automated driving in Europe. Starting from the motivations of automated driving this chapter reviews recent achievements in driver assistance systems and highlights promising paths of future development of automated driving, pointing out the research and innovation needs in key enabling technologies and also considering solutions for non-technical issues. Furthermore, potential synergies between the automation and the electrification of the vehicle are analyzed.
The purpose of this research was twofold: to investigate patterns of music listening among music majors regarding accompanied/unaccompanied excerpts varying in tone quality and intonation, and to investigate the reliability of the Continuous Response Digital Interface(CRDI). Present studies were the third and fourth in a series and were designed to replicate and extend previous studies that determine whether listeners demonstrate consistent listening patterns to musical excerpts intentionally structured to be perceived as "good" and "bad." Excerpts consisted of the first and second phrases of Schubert's and Gounod's "Ave Maria" performed by a soprano, tenor, violinist, and cellist with and without piano accompaniment. Results of the present investigations indicated that subjects (N=80 in each study) easily discriminated between the good and bad examples when focusing on both tone quality and intonation using a paper and pencil Likert-type(1-5) scale. There was also no significant difference between ratings for the accompanied versus the unaccompanied selections. Mean ratings were very similar to results found in the two previous studies. The correlation between the third replication with previous responses from subjects in studies one and two who responded either to intonation only or to tone quality only was .977 and .945 respectively. Additionally, spectrographic and other acoustical analyses of each rendition indicated that subjects responded differentially to the various excerpts with each example being perceived idiosyncratically within its own musical context.
This paper contains an overview, analysis, and comments regarding fifteen years of research using the Continuous Response Digital Interface (CRDI). A brief history of the CRDI development is given followed by a general explanation of issues relating to validity and reliability, especially concerning CRDI studies in music. Specific studies are addressed relating to assessments using this device in relationship to other continuous response devices and other traditional measurement scales.
For the past hundred years, innovation within the automotive sector has created safer, cleaner, and more affordable vehicles, but progress has been incremental. The industry now appears close to substantial change, engendered by autonomous, or "self-driving," vehicle technologies. This technology offers the possibility of significant benefits to social welfare — saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises. After surveying the advantages and disadvantages of the technology, RAND researchers determined that the benefits of the technology likely outweigh the disadvantages. However, many of the benefits will accrue to parties other than the technology's purchasers. These positive externalities may justify some form of subsidy. The report also explores policy issues, communications, regulation and standards, and liability issues raised by the technology; and concludes with some tentative guidance for policymakers, guided largely by the principle that the technology should be allowed and perhaps encouraged when it is superior to an average human driver.
Acceptance of new technology and systems by drivers is an important area of concern to governments, automotive manufacturers and equipment suppliers, especially technology that has significant potential to enhance safety. To be acceptable, new technology must be useful and satisfying to use. If not, drivers will not want to have it, in which case it will never achieve the intended safety benefit. Even if they have the technology, drivers may not use it if it is deemed unacceptable, or may not use it in the manner intended by the designer. At worst, they may seek to disable it. This book brings into a single edited volume the accumulating body of thinking and research on driver and operator acceptance of new technology. Bringing together contributions from international experts from around the world, the editors have shaped a book that covers the theory behind acceptance, how it can be measured and how it can be improved. Case studies are presented that provide data on driver acceptance of a wide range of new and emerging vehicle technology. Although driver acceptance is the central focus of this book, acceptance of new technology by operators in other domains, and across cultures, is also investigated. Similarly, perspectives are derived from domains such as human computer interaction, where user acceptance has long been regarded as a key driver of product success. This book comes at a critical time in the history of the modern motor vehicle, as the number of new technologies entering the modern vehicle cockpit rapidly escalates. The goal of this book is to inspire further research and development of new vehicle technology to optimise user acceptance of it; and, in doing so, to maximise its potential to be useful, satisfying to use and able to save human life. © Michael A. Regan, Tim Horberry and Alan Stevens and the contributors 2014. All rights reserved.
Abstract
A positive driving experience is a central factor for the success of an automobile. This article presents a model that explains the emergence of positive emotions when driving and discusses methods that can map the subjective perception of driving. On the basis of literature searches and own experimental studies, general guidelines for the design of vehicles for "fun of driving" / positive driving experience are derived.
Demography is the study of changes in the size, diversity, distribution and composition of human populations over time. The world’s age composition has changed dramatically and these changes continue. The percentage of individuals ≥65 years of age will double from 7 to 14 %, rising from 506 million in 2008 to 1.4 billion by 2040, with the largest increases in developing countries. It is important to note that the older population is getting older, with the largest increases in those ≥80 years of age. Life expectancy at age 65 has increased. In 2003, the average 65-year old woman in the United States was expected to live an additional 19.6 years, and a man, an additional 16.8 years. The older population is mostly female, especially in developed nations. Cardiovascular disease is the major cause of death worldwide. Disability in older adults is declining, though these trends may not continue given the exponential growth of the oldest old population. These demographic changes will profoundly impact public health. Cross-national research must address this unprecedented growth, specifically longitudinal studies to identify links between health, disability, economic status, work and family structure; to establish mechanisms to harmonize and standardize data collection internationally; and to develop multidisciplinary research designs to address issues impacted by population aging.