Conference Paper

Autonomous Vehicles: Human Factors Issues and Future Research

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Abstract

Automated vehicles are those in which at least some aspects of a safety-critical control function occur without direct driver input. It is predicted that automated vehicles, especially those capable of " driving themselves " , will improve road safety and provide a range of other transport and societal benefits. A fundamental issue, from a human factors perspective, is how to design automation so that drivers understand fully the capabilities and limitations of the vehicle, and maintain situational awareness of what the vehicle is doing and when manual intervention is needed – especially for first generation vehicles that require drivers to resume manual control of automated functions when the vehicle is incapable of controlling itself. The purpose of this paper is to document some of the human factors challenges associated with the transition from manually driven to self-driving vehicles, and to outline what we can be doing in Australia, through research and other means, to address them.

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... Automated vehicle technology has become much more advanced over recent years as the self-driving vehicle is starting to become technologically feasible (Funkhouser & Drews, 2016a;Gold, Damböck, Lorenz, & Bengler, 2013;. This technology is expected to have several benefits, such as increased road safety (Cunningham & Regan, 2015;Seppelt & Victor, 2016), energy efficiency (Seppelt & Victor, 2016), and driver comfort (Seppelt & Victor, 2016;Zeeb, Buchner, & Schrauf, 2016). But these automated vehicles are still not perfect and occasionally require the driver's assistance (NHTSA, 2013;Seppelt & Victor, 2016). ...
... But the system is also required to detect these boundaries (Zeeb et al., 2016), send the driver a take-over request (TOR), and is supposed to give the driver enough time to perform the necessary actions (NHTSA, 2013). This technology fundamentally changes the driver's role from an active operator to a passive monitor (Cunningham & Regan, 2015;Martens et al., 2008;. Some experts have stated that the driver does not even need to continuously monitor the system since they will be alerted when they are needed (NHTSA, 2013;Seppelt & Victor, 2016) and can engage in non-driving-related tasks if they wish to (Gold, Naujoks, et al., 2017). ...
... Studies have shown that engaging in secondary tasks during semiautonomous driving could lead to lower situational awareness (de Winter et al., 2014), higher reaction times to TORs (i.e., take-over time) (Borowsky & Oron-Gilad, 2016;Merat et al., 2012), shorter time-to-collision (Radlmayr, Gold, Lorenz, Farid, & Bengler, 2014), and higher lateral acceleration (i.e., rushed lane changes) during take-overs (Zeeb et al., 2016 However, multi-tasking during semi-autonomous driving could result in worse situational awareness than multi-tasking during manual driving. With lower situational awareness, drivers are less aware of potential hazards and are therefore less prepared for take-over scenarios (Cunningham & Regan, 2015). Merat, Jamson, Lai, Daly, et al. (2014) found that participants had worse driving performance when they were looking away from the road center since they were less able to predict oncoming take-overs. ...
Thesis
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Vehicle automation has become much more advanced, resulting in semi-autonomous vehicles which can handle the driving task by themselves under certain conditions. The autonomous driving technology is still limited, however, occasionally requiring driver intervention for events it cannot handle. It can be difficult for the driver to reorient themselves back to the driving task, especially if they are distracted. One factor in driving distraction is where the driver is looking at for the secondary task. If the in-vehicle display is placed closer to the driving scene (e.g., the windshield), drivers could spend less time scanning between the display and the road and can still monitor the driving environment with their peripheral vision. This dissertation aims to study how different in-vehicle display locations for a secondary reading task affect semi-autonomous driving performance. The main hypothesis was that display locations closer to the driving environment will allow drivers to better monitor the road and have better driving performance. Participants drove on a simulated highway using semi-autonomous driving, occasionally intervening for hazardous events. The display location near the CD-player (the location furthest from the driving environment) did result in slower reactions to one of the critical events when compared to one of the windshield display locations (closest to the driving environment). Overall, however, the location for the display seemed to have had little effect on driving performance. Although the location for the display may theoretically have an effect on how drivers monitor the road, these results suggest that it may not be enough to result in better driving safety for a semi-autonomous vehicle.
... The following three aspects have been identified: Re-engagement -In the event of system failure or other situations, the process of how efficiently and rapidly the driver takes over control from machine-driving is a key area. The time taken over is likely to be influenced by a combination of traffic density, driver experience and driver engagement in secondary tasks (Zeeb, Buchner, and Schrauf 2015) (Cunningham and Regan 2015). ...
... It is essential to keep the vehicle occupants 'in-the-loop' (awareness about the status of vehicles and road traffic situation). This is important to signal a safer re-engagement during emergencies (Cunningham and Regan 2015). ...
Article
Advanced Technologies are transforming the Automotive industry and the pace of innovation is accelerating at a breakneck speed. Autonomous Vehicles (AVs) incorporate many different systems and technologies and their increased computer functionality and connectivity lead to enormous cybersecurity risk. The aim of this research is to explore the significant factors that influence cyber threats on AVs and to examine their level of importance. Partial Least Squares path modeling was preferred for research studies for its flexible modeling and identifying key drivers. The data analysis was carried out using ADANCO 2.0.1 to develop and evaluate the structural model and the causal relationships between the variables. Correlation of in-vehicular network vulnerabilities with trust and the correlation between the “workload of the driverless system” with cyber-attacks and cyber threats to AVs are two relations but have not been touched upon in previous studies. In this research, a modified framework is proposed based on the Cyber Cycle and integrated model of Diamond Model of Intrusion Analysis with the Active Cyber Defense Cycle.
... Furthermore, due to their different nature and operational environment, different testing and validation (T&V) processes need to be explored with a number of identified major challenge areas presented in [3]. In more detail, the importance of proving ground testing [5], testing with the driver out of the loop [3] as well as the consideration of human factors-related variables [6] have been identified as essential requirements for CAV T&V. Additionally, the increased complexity of CAVs stresses the importance of developing T&V processes where qualities such as usability, traceability and time-to-market are acknowledged. ...
... When a federated approach is considered, two popular cosimulation standards used are the High-Level Architecture standard (HLA) [31] and the Functional Mock-Up Interface (FMI) [32], with the first mostly used in the aerospace industry and the second in the automotive industry. HLA is an architecture enabling distributed and parallel simulation [6], while FMI defines a standardized interface for coupling simulation tools in a co-simulation environment [33], suitable for the simulation of complex cyber-physical systems. ...
Conference Paper
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Testing & validation of high-level autonomy features requires large amounts of test data, which conventionally is achieved by accumulating miles on the road and dedicated proving grounds. This places an extreme burden not only on original equipment manufacturers (OEMs) of connected and autonomous vehicles (CAVs), but also on Tier 1 suppliers of CAV components, both in terms of cost and delivery time. To this end, multiple simulation platforms and techniques have emerged, such as hardware-in-the-loop testing methods, while the concept of co-simulation is gaining popularity as a more comprehensive solution for testing and validating CAVs. The aim of the DigiCAV project is to explore the feasibility of a co-simulation platform adopting a test-driven development approach for CAVs, by enabling a seamless testing and validation process across all stages of development, supporting a wide range of testing from model-in-the-loop of a CAV component all the way to vehicle-in-the-loop of a fully assembled vehicle on HORIBA MIRA's dedicated CAV proving ground test facilities. Furthermore, emphasis will be put on quality aspects such as testing accuracy, usability and protection of intellectual property rights. This paper introduces the DigiCAV project and disseminates results from its first deliverable focusing on capturing user requirements for the proposed simulation platform.
... Les facteurs sus-cités peuvent également avoir une influence néfaste sur la sécurité routière en impactant les capacités du conducteur pendant la conduite automatisée (Cunningham & Regan, 2015). Par exemple, une personne qui n'est pas cognitivement impliquée dans la tâche de conduite peut ressentir une fatigue passive qui va diminuer sa vigilance et donc la rapidité de ses réflexes en cas de demande de reprise en main de la part du système de conduite (Cunningham & Regan, 2015). ...
... Les facteurs sus-cités peuvent également avoir une influence néfaste sur la sécurité routière en impactant les capacités du conducteur pendant la conduite automatisée (Cunningham & Regan, 2015). Par exemple, une personne qui n'est pas cognitivement impliquée dans la tâche de conduite peut ressentir une fatigue passive qui va diminuer sa vigilance et donc la rapidité de ses réflexes en cas de demande de reprise en main de la part du système de conduite (Cunningham & Regan, 2015). ...
Thesis
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L’objectif principal de la présente thèse est d’étudier l’effet du niveau initial de confiance des conducteurs sur leurs comportements pendant la conduite automatisée, puis d’étudier la façon dont cette confiance va évoluer dynamiquement au cours du temps, pendant diverses situations en conduite automatisée. Ce travail de thèse s’intéresse d’abord aux facteurs qui déterminent le niveau initial de confiance des individus dans les systèmes de conduite complètement automatisée. Ces technologies étant encore jeunes et peu répandues, la confiance des conducteurs potentiels semble être influencée par la représentation mentale qu’ils s’en font, et qui peut être biaisée de différentes façons. La thèse s’intéresse ensuite au processus de construction dynamique de la confiance pendant les premières interactions entre les conducteurs et le système de conduite automatisée, ainsi qu’aux effets éventuels de déconstruction/reconstruction qui peuvent survenir à la suite de situations critiques ou de défaillances rencontrées pendant la conduite.
... Behavioral adaptation. In CAV system, Human-Machine Interface (HMI) plays a critical role as the HMI assists user to change user's role from an actuator to a supervisor or vice-versa [88,[116][117][118]. A user needs to adapt to the HMI interface of a CAV to execute appropriate decisions through voice command, touch or any other haptic (i.e., gesture) command. ...
... Recently, gesture-based automated interface has been explored using different sensor technologies for vehicle control, primarily for automated vehicles, along with voice and touch interface [121]. When an occupant or a user is unable to interact with the CAV system through voice or touch interface, a gesture-controlled system could be very effective [116]. A CAV vehicle user performs a gesture (e.g., the motion of hand), and the CAV can interpret and react in a manner that is commensurate with the users' intentions. ...
Article
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Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to their potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies; human factors; and information-aware controller design. First, the different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factors, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, the different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, the critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, the promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.
... I would feel safer relying on my own expertise and using [Violation product]. " These observations by the participants were consistent with other researchers' reports of users' attitudes, sometimes with them overly relying on the system's outputs [32] or becoming inattentive because of lightened workload or boredom [14]. Similarly, others have reported on users' attitudes reflecting an unwillingness to give up control without the ability to regain it, hesitant about an unproven technology [24,26]. ...
... Similarly, van der Groot & Pilgrim, reported that their participants' age influenced their Motivations to interact with technology [77]. 14 Table 11 in Appendix E. 15 Their correlation coefficient was significant even under = .01. ...
Preprint
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Artificial Intelligence (AI) is becoming more pervasive through all levels of society, trying to help us be more productive. Research like Amershi et al.'s 18 guidelines for human-AI interaction aim to provide high-level design advice, yet little remains known about how people react to Applications or Violations of the guidelines. This leaves a gap for designers of human-AI systems applying such guidelines, where AI-powered systems might be working better for certain sets of users than for others, inadvertently introducing inclusiveness issues. To address this, we performed a secondary analysis of 1,016 participants across 16 experiments, disaggregating their data by their 5 cognitive problem-solving styles from the Gender-inclusiveness Magnifier (GenderMag) method and illustrate different situations that participants found themselves in. We found that across all 5 cogniive style spectra, although there were instances where applying the guidelines closed inclusiveness issues, there were also stubborn inclusiveness issues and inadvertent introductions of inclusiveness issues. Lastly, we found that participants' cognitive styles not only clustered by their gender, but they also clustered across different age groups.
... It is necessary to study the human factors that determine the characteristics of the transition between automated and manual modes of driving for safe control authority transitions. These factors include the driver's inattention and distraction, situational awareness, over-reliance on ADS, and driving skill degradation [11]. These human factors can affect TOR performance, which can be divided into time-related and quality-related performances [9]. ...
Article
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In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.
... While drivers do not longer need to continuously monitor the situation in level 3, they still need to be aware of situations that might be outside the automations Operational Design Domain (ODD) as they need to be able to take back control within a relatively short time span if a take-over request is issued. This shift towards automation supervision has long shown to be difficult for humans (Bainbridge, 1983;Parasuraman, Molloy, & Singh, 1993) and is expected to induce multiple Human Factors issues including: erratic workload, distraction, complacency, reduced situational awareness, uncalibrated trust and incorrect mental models (Casner, Hutchins, & Norman, 2016;Cunningham & Regan, 2015;Martens & van den Beukel, 2013;Saffarian, De Winter, & Happee, 2012). These issues should be addressed as they all directly impact appropriate automation use, quality of take-overs, and consequently traffic safety. ...
... They identify these trends: from driver assistance to driving automation, from distraction to non-driving-related activities, from UI to UX design, and simulator versus naturalistic studies. [23] examine human factor issues related to autonomous driving: human inattention and distraction, situational awareness, overreliance and trust, skill degradation, motion sickness, re-engaging the driver, user interface and the communication of automation limitations, automation misuse and the need to monitor the driver, personalization of automation, and acceptance. [72] report similar challenges, but they describe them from a technical perspective. ...
Article
Full-text available
Automated vehicles (AVs) are on the edge of being available on the mass market. Research often focuses on technical aspects of automation, such as computer vision, sensing, or artificial intelligence. Nevertheless, researchers also identified several challenges from a human perspective that need to be considered for a successful introduction of these technologies. In this paper, we first analyze human needs and system acceptance in the context of AVs. Then, based on a literature review, we provide a summary of current research on in-car driver-vehicle interaction and related human factor issues. This work helps researchers, designers, and practitioners to get an overview of the current state of the art.
... They have the potential to improve many of our interactions with transportation systems through increased safety and public health from reduced traffic fatalities [7], lower environmental impacts [6], and increased mobility and independence for people who currently do not have physical or economics means to travel on their own [1]. This being said, all technology development comes with potential negative side effects, and autonomous vehicles present many human-factors, ethical, social, legal, security, and privacy challenges [1,[3][4][5]. Recent polls on public acceptance report that many people are aware of these challenges and feel uncertain about autonomous vehicles deployment [8,9]. ...
... However, their overview was not based on an SLR, and they did not consider some sensor/actuator categories, e.g., vestibular, olfactory, and gustatory. Besides, there are reviews regarding human factor-related issues (e.g., distraction, awareness, trust, or acceptance) [76,164] and technical challenges [157]. The first design space for driver-based automotive UIs was introduced by Kern and Schmidt [177] that describes in-vehicle input and output modalities concerning their location in the interior. ...
Preprint
Full-text available
Automotive user interfaces constantly change due to increasing automation, novel features, additional applications, and user demands. While in-vehicle interaction can utilize numerous promising modalities, no existing overview includes an extensive set of human sensors and actuators and interaction locations throughout the vehicle interior. We conducted a systematic literature review of 327 publications leading to a design space for in-vehicle interaction that outlines existing and lack of work regarding input and output modalities, locations, and multimodal interaction. To investigate user acceptance of possible modalities and locations inferred from existing work and gaps unveiled in our design space, we conducted an online study (N=48). The study revealed users' general acceptance of novel modalities (e.g., brain or thermal activity) and interaction with locations other than the front (e.g., seat or table). Our work helps practitioners evaluate key design decisions, exploit trends, and explore new areas in the domain of in-vehicle interaction.
... Unfortunately, AV technology is not yet completely reliable; therefore, the human driver has to take over the driving process, supervising, and monitoring the driving tasks when AV system fails or is limited by performance capability [69,72]. In turn, the shifting role of a human driver in AV driving may lead to inattention, reduced situational awareness, and manual skill degradation [73]. erefore, how to safely and effectively re-engage the driver when the autonomous systems fail should be considered in designing the AVs from a human-centered perspective. ...
Article
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Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10-4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014-2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.
... The increase of automation will change the behaviour of vehicles, due to a reaction to driver assistance [22], fully autonomous characteristics [23] and human response to autonomous vehicles [24]. Monitoring the behaviour of vehicles as witnessed through smart infrastructure is one mechanism through which cyber attacks may be identified. ...
Conference Paper
Full-text available
Connected and autonomous vehicles (CAVs) are an emerging technology that will introduce new threats to the general public. Impending standards (such as ISO21434) demonstrate that there is a real cyber security risk and a need for supporting infrastructure in the form of vehicle security operations centre. In this concept paper we discuss some of the issues facing vehicle security as the technology matures over the next few years and look at how epidemiological models for malware might be developed to address concerns over vehicle cyber threats. We detail our development of Mobius, a bespoke tool for simulating and analysing malware events in CAVs and explore how the technology might be applied to support real-world decision making. As a part of the need for cyber resilience, we suggest there is a key role for vehicle simulation software capable of modelling cyber threats to assist with threat analysis and decision making for highway authorities, OEMs and fleet operators, amongst others. We present a summary of compartmental epidemiological models and the role they can play in understanding malware propagation for CAVs.
... An assumption is made that the driver will respond to a prompt to appropriately intervene when necessary. Though a responsive driver is a presumption of the SAE J3016 standard, viability of this rational is controversial [20], [21]. The operational flow of a Level 3 domain controller is presented in Figure 5. ...
... As people become more familiar with a piece of technology, they can become acclimated to it. This acclimation can either be positive, as users become more comfortable or trusting in the technology, leading to wider acceptance, or it can be a negative, as users become over-reliant on the technology and misuse it (Parasuraman & Riley, 1997;Cunningham & Regan, 2015;Sauer, Chavaillaz, & Wastell, 2016). ...
Thesis
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One of the greatest barriers to the entry of highly automated vehicles (HAV) into the market is the lack of user trust in the vehicle. Research has shown that this lack of faith in the system primarily stems from a lack of system transparency while in motion (e.g., the user not being told how the car will react in a certain situation) and not having an effective way to control the vehicle in the event of a system failure. This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to first appear and where the user has less training and control over the vehicle. To improve user trust and perceptions of comfort and safety, this study developed human-machine interface (HMI) systems, focusing on visual and auditory displays, to better relay automated vehicle “intentions” and the perceived driving environment to the user. These HMI systems were then implemented into a HAV developed at the Virginia Tech Transportation Institute (VTTI) and tested with volunteer participants on the Smart Roads.
... When it comes to autonomous cars, Bainbridge's ironic statement could sound inaccurate; however, before reaching fully automated or driverless cars, the automation level will actually increase in parallel with the necessity of a human driver ready to take over. According to Cunningham and Regan [2015] the main human factor issues associated with partially automated driving are drivers' inattention and distraction, reduced situational awareness, manual skill degradation and motion sickness. Kyriakidis et al. [2017] presented the perspective of researchers in the field of Human Factors (HF) and AVs. ...
Thesis
Driving automation is an ongoing process that is radically changing how people travel and spend time in their cars during journeys. Conditionally automated vehicles free human drivers from the monitoring and supervision of the system and driving environment, allowing them to perform secondary activities during automated driving, but requiring them to resume the driving task if necessary. For the drivers, understanding the system’s capabilities and limits, recognizing the system’s notifications, and interacting with the vehicle in the appropriate way is crucial to ensuring their own safety and that of other road users. Because of the variety of unfamiliar driving situations that the driver may encounter, traditional handover and training programs may not be sufficient to ensure an effective understanding of the interaction between the human driver and the vehicle during transitions of control. Thus, there is the need to let drivers experience these situations before their first ride. In this context, Mixed Reality provides potentially valuable learning and skill assessment tools which would allow drivers to familiarize themselves with the automated vehicle and interact with the novel equipment involved in a risk-free environment. If until a few years ago these platforms were destined to a niche audience, the democratization and the large-scale spread of immersive devices since then has made their adoption more accessible in terms of cost, ease of implementation, and setup. The objective of this thesis is to investigate the role of Mixed Reality in the acquisition of competences needed for a driver’s interaction with a conditionally automated vehicle. In particular, we explored the role of immersion along the Mixed Reality continuum by investigating different combinations of visualization and manipulation spaces and the correspondence between the virtual and the real world. For industrial constraints, we restricted the possible candidates to light systems that are portable, cost-effective and accessible; we thus analyzed the impact of the sensorimotor incoherences that these systems may cause on the execution of tasks in the virtual environment. Starting from these analyses, we designed a training program aimed at the acquisition of skills, rules and knowledge necessary to operate a conditionally automated vehicle. In addition, we proposed simulated road scenarios with increasing complexity to suggest what it feels like to be a driver at this level of automation in different driving situations. Experimental user studies were conducted in order to determine the impact of immersion on learning and the pertinence of the designed training program and, on a larger scale, to validate the effectiveness of the entire training platform with self-reported and objective measures. Furthermore, the transfer of skills from the training environment to the real situation was assessed with test drives using both high-end driving simulators and actual vehicles on public roads.
... Further, autonomous cars still suffer from frequent system failures and therefore require driver's assistance [10]. Taken together, there is a need to make drivers aware of the traffic, limitations of the system, and when to take control over the driving [7]. ...
Conference Paper
Semiautonomous driving still requires the driver's control and attention in certain situations. Especially control transitions, i.e. take-over and hand-over situations, are important for safety. Our aim was to study control transitions supported by unimodal (i.e. visual, auditory, or haptic) or multimodal (i.e. visual, auditory and haptic) signals indicating change from manual to autonomous driving and vice versa. The signals were abstract visual blinks, auditory beeps, or haptic vibrations. The task was to take over driving while either looking through the windshield or playing a game. In addition, in half of the control transitions a feedback signal indicated successful control transition. The results showed that a secondary task slowed down the reaction times, but there was a great variation between individuals. In general, the response to auditory signal was slower than to visual, haptic, or multimodal signals. Moreover, users preferred feedback during control transitions but this slowed down the reaction time.
... However, in case of an emergency, an automation level equivalent to Society of Automotive Engineers (SAE) 2 or 3 requires the driver to be ready to resume control within the shortest time possible, as the vehicle gets out of its Operational Design Domain (ODD) -the specific conditions under which a given automation system is designed to function [6]. Due to the lack of active involvement in the driving task, drivers may feel a natural tendency to engage in non-driving related activities, which reduces their alertness and, consequently, the ability to perform the fallback operation [7] -the response given by the driver to achieve a minimal risk condition as the automation system leaves its ODD [6]. Some studies proved that the quality of the fallback process decreases as the complexity of driving non-related tasks increase [8,9]. ...
Article
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The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle’s control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver’s state with an accuracy ranging from 89% to 93%.
... Researchers expect that fully automated vehicles (AVs; i.e., self-driving cars) may allow older adults to live more active, independent, and healthy lifestyles (Duarte & Ratti, 2018;Reimer, 2014;Yang & Coughlin, 2014). The use of AVs could provide a promising solution to some of the mobility barriers that older adults experience and potentially even afford them the ability to reenter the workforce (Harper et al., 2016;Yankelevich et al., 2018), though AV services may need to be specifically designed to cater to the needs and preferences of older adults (Cunningham & Regan, 2015;Payyanadan & Lee, 2018;Rhiu et al., 2015). However, about 75% of American adults are still apprehensive about using AVs (Edmonds, 2019). ...
Article
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The diffusion of fully automated vehicles (AVs), or self-driving vehicles, is expected to provide many affordances for older adults. If older adults are not willing to use AVs, they will not be able to reap these affordances. Understanding factors related to older adults’ willingness to use AVs is key to ensuring that successful strategies can be devised to promote their utilization in the future. In this study, we investigate U.S. older adults’ willingness to use AVs among a large and diverse sample ( N = 1,231). We assessed sociodemographic, population density, health, and attitudinal determinants of willingness to use AVs. Our binary logistic regression results showed that older adults with higher levels of educational attainment, transportation limitations, and positive attitudes toward new technology adoption were more likely to be willing to use AVs. Our study indicates that older adults’ willingness to use AVs are complex and vary among U.S. older adults. Practical implications and study limitations are discussed.
... Research on automated cars and automated driving shows that automation leads to a reduction in drivers' workload, but this leads to passive fatigue among drivers, leading to a reduction in overall performance [56]. Remote ship operators are also at risk of active and passive fatigue. ...
Article
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The idea of remote controlling ships for operational and commercial uses has developed beyond concepts. Controlling and monitoring vessels from a distant location requires updating the concept and requirements of shore control centers (SCCs), where human operators control the fleet via cameras, GPS, and many other types of sensors. While remote ship operation promises to reduce operational and maintenance costs, while increasing loading capacity and safety, it also brings significant uncertainty related to both the human-machine and human-human interactions which will affect operations. Achieving safe, reliable, and efficient remote ship operations requires consideration of both technological, cultural, social and human factor aspects of the system. Indeed, operators will act as captain and crew remotely, from the SCC, introducing new types of hardware and software interactions. This paper provides an overview of human factor issues that may affect human-machine and human-human interactions in the course of remote ship operations. In doing so, the literature related to remote operations in the domains of shipping, aerial vehicles, cranes, train transportation, automobiles, and mining is reviewed. Findings revealed that human factor issues are likely to fall into 13 distinct groups based on the type of human interactions that take place in SCCs.
... Operational risk exists when both systems simultaneously perceive the other to be the ultimate controlling authority. Across all levels of automation, there is the potential for the operator to misunderstand his/ her authority and responsibilities within the OVI system, which may be associated with a safety-critical incident [50]. ...
Chapter
The most frequent justification for implementing automated vehicles is the claim that they will increase road safety by removing human involvement in driving. This, however, introduces emerging Human Factors (HFs) issues, since regardless of the level of automation, the human being will continue to play a crucial role in interacting with vehicle automation. In the medium-low levels, the driver will have to play a supervisory role which will introduce out-of-the-loop problems, in the driver-vehicle interaction during the transition of control. At the higher level, new forms of accidents may occur associated with the need for automated vehicles to interact with other road users. The chapter is a thorough literature review of the HFs for both of these interactions, mainly those relating to the medium-low level of automation. Such review is aimed at understanding the influences of HFs on road safety and the role played by infrastructures.
... Besides the advantages, autonomous vehicle researchers face an unavoidable challenge which is the user acceptance. User acceptance is important because it determine whether the vehicle will be used by the public or not [11]. The critical issue that need to be overcome to build the trust from the public is regarding the safety [12]. ...
Chapter
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
... However, their overview was not based on an SLR, and they did not consider some sensor/actuator categories, e.g., vestibular, olfactory, and gustatory. Besides, there are reviews regarding human factor-related issues (e.g., distraction, awareness, trust, or acceptance) [76,164] and technical challenges [157]. The first design space for driver-based automotive UIs was introduced by Kern and Schmidt [177] that describes in-vehicle input and output modalities concerning their location in the interior. ...
Article
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Automotive user interfaces constantly change due to increasing automation, novel features, additional applications, and user demands. While in-vehicle interaction can utilize numerous promising modalities, no existing overview includes an extensive set of human sensors and actuators and interaction locations throughout the vehicle interior. We conducted a systematic literature review of 327 publications leading to a design space for in-vehicle interaction that outlines existing and lack of work regarding input and output modalities, locations, and multimodal interaction. To investigate user acceptance of possible modalities and locations inferred from existing work and gaps unveiled in our design space, we conducted an online study (N=48). The study revealed users' general acceptance of novel modalities (e.g., brain or thermal activity) and interaction with locations other than the front (e.g., seat or table). Our work helps practitioners evaluate key design decisions, exploit trends, and explore new areas in the domain of in-vehicle interaction.
... Public acceptance of CAVs is, therefore, of paramount importance, as it will determine whether the systems will actually be used (Cunningham & Regan, 2015). If connected automated driving is perceived as unacceptable, vehicle users may refuse to use it and negate all associated benefits (Ekman, Johansson, & Sochor, 2016). ...
Conference Paper
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"One of the main causes of lack of acceptance in innovation is ignoring the needs and preferences of potential customers in the development phases. In the case of the connected automated vehicle (CAV), there is an important degree of user skepticism based on the awareness of the complexity and the risks of this technology. Public acceptance is a multi-faceted construct, tightly related to emotional processes and trust in a new technology, beyond the accomplishment of functional performance. However, the current approach based on the technology push threatens social viability of innovative technology like CAV, as it creates a gap between the well-thought technical reliability and public acceptance. The H2020 project SUaaVE (SUpporting acceptance of automated VEhicle) aims to make a change in the current situation of public acceptance of CAV. SUaaVE formulates a new concept called ALFRED, a human centered artificial intelligence to humanize the vehicle actions by understanding the emotions of the passengers of the CAV while also managing corrective actions in vehicle for enhancing trip experience. In line with this research, the H2020 project DIAMOND (Revealing fair and actionable knowledge from data to support women’s inclusion in transport systems) seeks to generate knowledge from data for more inclusive and efficient transport systems, being one of the main objectives to enhance the acceptance of women using and driving automated vehicles. This paper presents the main results obtained of two experimental tests carried out in each of the two projects. More than 50 subjects participated in each test, experiencing different scenarios of L4 automated vehicles in an immersive dynamic driving simulator. In both tests, the physiological response of the participants was measured (HR, EDA and facial EMG), considering other additional biometrics (breathing rate, temperature, sweating) and behavioral (facial expression, blinking, etc.) in the case of SUaaVE project. In case of DIAMOND, the experimentation was focused on estimating the participant emotional state, arousal and valence, by HR, EDA and facial EMG in autonomous driving scenarios. The goal was to explore the influence of gender and related intersectional variables in the emotional response that could lead to autonomous vehicle acceptance. The analysis of the test in SUaaVE has allowed a scientific advance defining an emotional model based on the contextual factors involving the experience in the Ego Car - The trip purpose (work travel, day shift, holidays, etc.) and the state of road (density of cars, weather conditions, safety envelop, etc.) – together with complete monitoring the passenger’s physiology and behavior. The approach presented will facilitate that automated vehicles are able to understand how we feel and use such information to make system more empathic, responding to the occupant emotions in real time. This will allow to OEMs and Tier 1 suppliers a detailed characterization of the passenger needs, enabling them the development of strategies to enhance the in-cabin experiences and, in case of DIAMOND project, include needs in women in CAV deployment strategies."
... In addition, autonomous cars are just entering the market and are expected to grow and account for 15 percent of total cars sold by 2030 ("Automotive Revolution & Perspective Towards 2030", 2016). Prior to the large-scale introduction and adoption of autonomous vehicles, a number of underlying factors related to safety and trust in these systems must be evaluated and applied (Cunningham and Regan, 2015). For instance, with the recent reported crashes of semi-autonomous vehicles, serious concerns have been raised about how these vehicles can be fully adopted and trusted by the consumer (Higgins, Spector, and Colias, 2018). ...
Conference Paper
Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence driver’s emotional state and, accordingly, driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of the driver/passenger(s)’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on driver’s emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s emotion is highly affected by the type of road, presence of a passenger, and weather condition, which potentially can change the driving behaviors. For instance, by defining emotions metrics as valence and engagement, there exist significant differences between human emotion in different weather conditions and road types. Participant’s engagement was higher in rainy and clear weather compared to cloudy weather. Moreover, his engagement was higher in city streets and highways compared to one lane roads and two lane highways. In addition, presence of a passenger increases the amount of engagement of the driver.
... One such type of automation misuse potentially leading to dangerous situations when interacting with AVs is an "overreliance" on the automation system (Parasuraman and Manzey, 2010). Overreliance occurs when a driver tends to rely uncritically on the automation without recognizing its limitations or fails to monitor the automation system's behavior (Saffarian et al., 2012;Cunningham and Regan, 2015). ...
Article
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Some studies provide evidence that humans could actively exploit the alleged technological advantages of autonomous vehicles (AVs). This implies that humans may tend to interact differently with AVs as compared to human driven vehicles (HVs) with the knowledge that AVs are programmed to be risk-averse. Hence, it is important to investigate how humans interact with AVs in complex traffic situations. Here, we investigated whether participants would value interactions with AVs differently compared to HVs, and if these differences can be characterized on the behavioral and brain-level. We presented participants with a cover story while recording whole-head brain activity using fNIRS that they were driving under time pressure through urban traffic in the presence of other HVs and AVs. Moreover, the AVs were programmed defensively to avoid collisions and had faster braking reaction times than HVs. Participants would receive a monetary reward if they managed to finish the driving block within a given time-limit without risky driving maneuvers. During the drive, participants were repeatedly confronted with left-lane turning situations at unsignalized intersections. They had to stop and find a gap to turn in front of an oncoming stream of vehicles consisting of HVs and AVs. While the behavioral results did not show any significant difference between the safety margin used during the turning maneuvers with respect to AVs or HVs, participants tended to be more certain in their decision-making process while turning in front of AVs as reflected by the smaller variance in the gap size acceptance as compared to HVs. Importantly, using a multivariate logistic regression approach, we were able to predict whether the participants decided to turn in front of HVs or AVs from whole-head fNIRS in the decision-making phase for every participant (mean accuracy = 67.2%, SD = 5%). Channel-wise univariate fNIRS analysis revealed increased brain activation differences for turning in front of AVs compared to HVs in brain areas that represent the valuation of actions taken during decision-making. The insights provided here may be useful for the development of control systems to assess interactions in future mixed traffic environments involving AVs and HVs.
... These levels also allow researchers to design for the appropriate human-automation interaction required at each level. The middle levels of autonomy are rife with human factors challenges; in SAVs humans will need to provide a supervisory role and engage in active interactions when the vehicle is unable to decide (Cunningham, 2015). While some have advocated for skipping to full autonomy (Davies, 2017) to bypass these challenges, the full potential of these technologies will only be realized by understanding and designing for the dynamics between humans and automation. ...
Article
This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.
... Current design and development of autonomous systems for military use have shown limited system adaptability to users' expertise and roles that run the risk of interfering or diminishing the user's capability (Militello & Klein, 2013), in the form of diminished manual skills (Cunningham & Regan, 2015) or loss of situation awareness (i.e., human out of the loop phenomenon; Endsley & Kiris, 1995). Moreover, automated systems designed for mission critical environments have been found to provide more capabilities than users can comprehend or use (Strauch, 2017) that may result in increased user workload or "automation surprises" if the automated system performs unexpectedly (Kaber et al., 2006;Sarter et al., 1997). ...
Article
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With Industry 4.0 the immense progression of Artificial Intelligence (AI) technology has introduced new challenges for engineers to effectively design human-automation interaction in autonomous systems that are mission critical. Although various autonomous systems are currently being utilized in mission critical environments, there is limited literature and research on which factors affect the acceptance and adoption of said systems. Understanding which factors are most critical for the human-automation interaction could lead to seamless acceptance and adoption and more effective and less expensive missions. Findings of 47 semi-structured interviews revealed ease of use and system reliability to be significant factors for the acceptance and adoption of autonomous systems independent of the level of automation. Through our findings we expand on the current technology acceptance models by including mission critical factors. Emphasis is given to the discussion and consideration of the human factors and engineering approaches associated with the design of autonomous systems for mission critical environments that are needed to empower tomorrow’s users with effective AI systems technology.
... Although much research has evaluated human performance issues related to driving or supervising a partially automated car [28], [29], few studies have investigated automated features for specific vehicle maneuvers, such as automated parking, in their own right, even though these features are now commonly included in many commercially available vehicles today. As vehicle manufacturers continue to build increasingly complex autonomous features, the need to critically evaluate these systems and their implications for users, owners, communities, and society, will grow in kind. ...
Preprint
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In two studies, we evaluated the trust and usefulness of automated compared to manual parking using an experimental paradigm and by surveying owners of vehicles with automated parking features. In Study 1, we compared participants' ability to manually park a Tesla Model X and use the Autopark feature to complete perpendicular and parallel parking maneuvers. We investigated differences in parking success and duration, intervention behavior, self-reported levels of trust in and workload associated with the automation, as well as eye and head movements related to monitoring the automation. We found higher levels of trust in the automated parallel parking maneuvers compared to perpendicular parking. The Tesla’s automated perpendicular parking was found to be less efficient than manually executing this maneuver. Study 2 investigated the frequency with which owners of vehicles with automated parking features used those features and probed why they chose not to use them. Vehicle owners reported using their vehicle's autonomous parking features in ways consistent with the empirical findings from Study 1: higher usage rates of autonomous parallel parking. The results from both studies revealed that 1) automated parking is error-prone, 2) drivers nonetheless have calibrated trust in the automated parking system, and 3) the benefits of automated parallel parking surpass those of automated perpendicular parking with the current state of the technology.
... For example, the desired possibility to engage in NDRTs (Pfleging, Rang, & Broy, 2016) that begins with Level 3 may have a substantial positive impact on the acceptance of AD. Potential safety risks due to the aforementioned Take-Over Requests (Cunningham & Regan, 2015) at this level could decrease acceptance though, in comparison to later arriving but safer Level 5 vehicles. Moreover, the degree of complexity required to describe these aspects accurately (in accordance with the existing standards by SAE, BASt or the NHTSA; cf. also Seppelt et al., 2019;Stayton & Stilgoe, 2020;Yang, Han, & Park, 2017) may lead to confusion in the non-expert public, or even abuse in advertisement (Dixon, 2020). ...
Article
Automated driving comes with many promises like zero traffic casualties that are, however, only realizable given their technological development and public acceptance for wide-spread deployment. To investigate the potential acceptance, we developed a new data-driven ques-tionnaire focusing on drivers and barriers of the anticipated possible (non-)adoption of automated driving (AD). Therefore, we conducted a cross-sectional questionnaire study with 725 re-spondents (351 female, 374 male) ranging from 18 to 96 years. We applied exploratory and confirmatory factor analyses and structural equation modeling, to pursue the overarching goal to develop the QAAD questionnaire (short and long version for SAE Level 3 (L3) and 5 (L5) AD). Hence, we identified the three latent factors PRO (positive aspects), CON (negative aspects), and NDRTs (non-driving related tasks) of L3 (short: 9 items; long: 16) and L5 (short: 11, long: 17), respectively. Additionally, we queried general questions on AD (8 items) and extracted the two factors Early Adoption/Pro AD and Sustainability. Our findings and the goodness-of-fit indices suggest data-driven models for L3 and L5 automated driving and on general aspects focusing on early adoption and sustainability in the context of AD. They can be applied in future research settings, in particular, in (quasi-)experimental L3 and L5 AD studies and in population surveys on AD. The evidence of the presented study should be validated and compared to other question-naires on AD in different countries around the globe.
... It has many disadvantagesdependency on human being populated areas or longdistance travel, chances of accident because of driver error and behavior resulting in fatality. An autonomous vehicle is a driverless vehicle which is able to operate without any human intervention [1]- [2], through the ability to sense it's surrounding and take the input data from the surrounding through sensors mounted on the vehicle. The steering control operates in such a way that considering all the factors a human would consider while driving a vehicle, the steering control system implements the same conditions and factors to operate the autonomous vehicle in an efficient way. ...
Article
Automation can help us to reduce the number of crashes on our roads. Through research it is identified that 94 percent of the accidents that occur are because of driver behavior or error as a factor and self-driving vehicles can help reduce driver error. High levels of automation have the potential to reduce risky and dangerous driver behavior and prevent accidents. The main aim is to convert the manual operated steering of the vehicle into fully autonomous steering. The objective of Steering Control System is to control the vehicle’s steering while the vehicle is in motion and also to take accurate decisions while making a turn from the given inputs. The main purpose to develop a steering system for the autonomous vehicle is to replace the manual steering of the vehicle into driverless steering. The steering control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn. A PID (Proportional Integral Derivative) controller and an encoder is basically used to control the system based on the necessary conditions and requirement’s. For the vehicle’s steering the encoder is used to generate pulses when the steering wheel is turned so that those pulse values can be sent to the DC Motor which is attached to the front axle which is responsible for the vehicle to turn. This autonomous vehicle is a Level-4 automation system and the benefit of this automation is that the vehicle can be even operated in manual mode whenever it’s necessary.
... Carrying this out may not be easy: the operator has to know the abilities, shortcomings, and limitations of their own and those of the system. Moreover, a high level of vigilance is needed to recognize the errors or malfunctions [7]. Thus, this implies the critical issue of cognitive and other skills in the context of driving. ...
Article
Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users.
Article
Automated vehicles are expected to communicate with pedestrians at least during the introductory phase, for example, via LED strips, displays, or loudspeakers. While these are added to minimize confusion and increase trust, the human passenger within the vehicle could perform motions that a pedestrian could misinterpret as opposing the vehicle’s communication. To evaluate potential solutions to this problem, we conducted an online video-based within-subjects experiment (N = 59). The solutions under evaluation were mode distinction, vehicle appearance, and the visibility of the passenger via a tintable windshield. Our results show that especially the mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects. A tintable windshield, however, was negatively assessed. Thus, our work helps to design eHMI concepts to introduce automated vehicles safely by informing about feasible methods to avoid mode confusion.
Chapter
Driving automation leads to meaningful changes of driver roles, from the primary party responsible for execution of all dynamic driving tasks to supervision of selective tasks in automated driving systems with varying levels of automation. In partially automated systems, drivers are required to resume control occasionally, either voluntarily or involuntarily. This paper aims at exploring human factors influencing the course of take-over. Through a review of a large body of literature and a summary of observations, some particularly influential driver-related issues are identified. These issues include mental workload and distraction, situation awareness, and trust. Based on the consideration of these issues, the timing and the efficiency of take-over are analyzed.
Chapter
The formal understanding of motion sickness has refined over the years, peculiarly in context of land vehicles. Since land vehicles are on-going a transitional phase in technology the perception of how motion sickness impacted passengers of land vehicles in the twentieth century to the twenty-first century has kept evolving in leaps and bounds. The problems that were previously faced or that had not surfaced up earlier will indeed transgress as we move up in the order or level of automation in land vehicles. Hence, this chapter elaborates how motion sickness was perceived in the early versions of land vehicles and how it modified as and when technology in cars made them more ride comfortable, and what we predict can be faced in context to motion sickness of passengers in Autonomous Cars.
Article
Autonomous vehicle (AV) attracts a lot of research interest because of their ability to reduce road fatalities and save people's lives. Pedestrian detection and collision avoidance are the crucial parts of AVs. The existing pedestrian detection systems don't guarantee better accuracy under complex scenarios, poor light conditions, and low complexity overhead. Moreover, these systems rely on monocular camera object detection. Hence, this paper aims to propose a stereo-vision based pedestrian detection and collision avoidance system for AVs. It uses two cameras fixed at a specific distance apart to scan the environment. Once a pedestrian is detected, the system calculates the distance. The Automatic Emergency Braking System (AEBS) controller algorithm will activate the AEB if the estimated distance is less than 3.3 m, considered safe. MATLAB is used for the implementation, and the experimental results reveal that the proposed method is promising in terms of prediction accuracy and minimizing fatalities.
Chapter
The biggest challenge for a human-machine interface in highly automated vehicles is to provide enough information to the potentially unaware human operator to induce an appropriate response avoiding cognitive overload. Current interface design struggles to provide timely and relevant information tailored for future driver’s needs. Therefore, a new human-centered approach is required to connect drivers, vehicles and infrastructures and account for non-driving related activities in the forthcoming automated vehicles. A viable solution derives from a holistic approach that merges technological tools with human factors knowledge, to enable the understanding and resolution of potential usability, trust and acceptance issues. In this paper, the human factors challenges introduced by automated driving provide the starting point for the conceptualization of a new Fluid interface. The requirements for the new concept are derived from a systematic analysis of the necessary interactions among driver, vehicle and environment. Therefore, the characteristics, components and functions of the interface are described at a theoretical level and compared to alternative solutions.
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The development of advanced technology has revolutionized human life. In this regard, autonomous driving, a core technology currently being developed, is changing rapidly. In addition to improving technology, the acceptance of technology users must be secured. Most relevant studies conducted hitherto have involved evaluation using acceptance elements defined based on the technology acceptance model and the unified theory of acceptance and use of technology. In this study, 21 elements associated with the acceptance of autonomous driving are defined. The Kano model is used to classify the acceptance elements into five attributes and to propose guidelines for improving acceptance. Driver characteristics are classified based on four human factors, which are used to investigate differences in acceptance between groups. A Google survey and fieldwork were completed by 187 participants. Contrary to previous studies, no significant gender differences are observed in the current study. In terms of age, many obstacles are encountered in securing autonomous driving acceptance from the elderly driver group. Additionally, a more conservative tendency is indicated by people with more driving experience. The results of this study reveal important points for identifying elements that hinder future sustainability and commercialization of autonomous driving, thereby facilitating its further technological development.
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Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers' takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants' takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants' takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers' visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants' attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers' situation awareness in critical situations.
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Changing lane must not only ensure the safety of the vehicle itself, but also ensure the patency of the traffic flow of the original lane and the target lane. Therefore, successful lane-changing is a key technology for autonomous vehicle control. In order to avoid collisions and ensure the smooth flow of traffic, in this paper a vehicle dynamics state model with time variable is established as plant, and the lateral force of the steering wheel is further optimized through Model Predictive Control(MPC), and then the steering wheel angle is obtained to complete the lane-changing operation. The longitudinal and lateral logic controllers designed through soft constraints can better achieve the results of successful lane-changing and unsuccessful return to the original lane, and the lane-changing characteristics within the safety corridor are analyzed in several ways. The simulation analysis of lane-changing strategy at different vehicle velocities provides helpful guidance for the design of autonomous vehicle controllers.
Technical Report
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In March 2019 we organized the 1st International Workshop on Autonomous System Safety (#IWASS) in Trondheim, Norway. We gathered over 40 subject matter experts from academia and industry. For three days we discussed challenges concerning autonomous systems safety, and possible solutions. The proceedings of the 1st IWASS are now available (also at https://lnkd.in/d4v7hsg). They document the discussions we had during the workshop, and we believe it provides a valuable reference for the field.
Book
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Nothing has been more prolific over the past century than human/machine interaction. Automobiles, telephones, computers, manufacturing machines, robots, office equipment, machines large and small; all affect the very essence of our daily lives. However, this interaction has not always been efficient or easy and has at times turned fairly hazardous. Cognitive Systems Engineering (CSE) seeks to improve this situation by the careful study of human/machine interaction as the meaningful behavior of a unified system. Written by pioneers in the development of CSE, Joint Cognitive Systems: Foundations of Cognitive Systems Engineering offers a principled approach to studying human work with complex technology. The authors use a top-down, functional approach and emphasize a proactive (coping) perspective on work that overcomes the limitations of the structural human information processing view. They describe a conceptual framework for analysis with concrete theories and methods for joint system modeling that can be applied across the spectrum of single human/machine systems, social/technical systems, and whole organizations. The book explores both current and potential applications of CSE illustrated by examples. Understanding the complexities and functions of the human/machine interaction is critical to designing safe, highly functional, and efficient technological systems. This is a critical reference for students, designers, and engineers in a wide variety of disciplines.
Conference Paper
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This desktop driving simulator study investigated the effect of engagement in a reading task during vehicle automation on drivers' ability to resume manual control and successfully avoid an impending collision with a stationary vehicle. To avoid collision, drivers were required to regain control of the automated vehicle and change lane. The decision-making element of this lane change was manipulated by asking drivers to move into the lane they saw fit (left or right) or to use the colour of the stationary vehicle as a rule (blue – left, red – right). Drivers' reaction to the stationary vehicle in manual control was compared to two automation conditions: (i) when drivers were engaged and observing the road during automation, and (ii) when they were reading a piece of text on an iPad during automation. Overall, findings suggest that drivers experiencing automation were slower to identify the potential collision scenario, but once identified, the collision was evaded more erratically and at a faster pace than when drivers were in manual control of the vehicle. Short (1-minute) periods of automation used in this study did not appear to impede drivers' ability to complete simple operational and tactical-level driving tasks, following a system initiated takeover request. Results suggest that until there is an effective strategy to help drivers regain situation awareness during resumption of control from Highly Automated Driving, they should be encouraged to remain in the driving loop.
Conference Paper
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The goal of this paper is to outline human-factors issues associated with automated driving, with a focus on car following. First, we review the challenges of having automated driving systems from a human-factors perspective. Next, we identify human-machine interaction needs for automated vehicles and propose some available solutions. Finally, we propose design requirements for Cooperative Adaptive Cruise Control.
Conference Paper
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Recent technological developments have shown a transition from informative driving support systems to more automated vehicles. Although automated vehicles are designed to overcome limitations in human perception, decision making and response, there may be a downside to introducing these technologies. The downside is based on the new cooperation between the driver and the vehicle, leaving room for misinterpretation, overreliance on system performance and loss of situation awareness in case of requested transfer of control from the automated vehicle back to the driver. This article raises several human factors issues that are of importance when designing (semi-)automated vehicles, such as: the driver as a system monitor, situation awareness and system limitations. Various implications for the design of automated systems are discussed.
Conference Paper
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Highly automated driving may improve driving comfort and safety in the near future. Due to possible system limits of highly automated driver support, the driver is expected to take over the vehicle control, if a so-called take-over request is issued. One example of these system limits are missing or ending lines on motorways. This study focuses on the design of take-over requests in such situations. Using a motion-based driving simulator, N = 16 participants encountered different take-over situations in congested traffic that varied in their difficulty: ending lines on straight road (easy), temporary lines due to a work zone (moderate) and loss of lines in a situation with high curvature (difficult). The driver support consisted of a hands-off system that was taking over longitudinal and lateral control. Participants were asked to perform a secondary task while driving. Take-over requests were presented either visually or visual-auditory. Drivers’ hands-on times (i.e., time until driver puts hands back on the steering wheel) are lower if visual-auditory take-over requests are used in comparison to purely visual ones. Measures of lateral vehicle control also show an advantage of visual-auditory take-over requests. Differences between the take-over concepts are especially pronounced in difficult take-over situations.
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A driving simulator study was designed to investigate drivers’ ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were looking away from the road ahead. In addition to studying the time it took drivers to successfully resume control from the automated system, eye tracking data were used to observe visual attention to the surrounding environment and the pattern of drivers’ eye fixations as manual control was resumed in the two conditions. Results showed that drivers’ pattern of eye movement fixations remained variable for some time after automation was switched off, if disengagement was actually based on drivers’ distractions away from the road ahead. When disengagement was more predictable and system-based, drivers’ attention towards the road centre was higher and more stable. Following a lag of around 10 s, drivers’ lateral control of driving and steering corrections (as measured by SDLP and high frequency component of steering, respectively) were more stable when transition to manual control was predictable and based on a fixed time. Whether automation transition to manual was based on a fixed or variable interval, it took drivers around 35–40 s to stabilise their lateral control of the vehicle. The results of this study indicate that if drivers are out of the loop due to control of the vehicle in a limited self-driving situation (Level 3 automation), their ability to regain control of the vehicle is better if they are expecting automation to be switched off. As regular disengagement of automation is not a particularly practical method for keeping drivers in the loop, future research should consider how to best inform drivers of their obligation to resume control of driving from an automated system.
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Despite the known dangers of driver fatigue, it is a difficult construct to study empirically. Different forms of task-induced fatigue may differ in their effects on driver performance and safety. Desmond and Hancock (2001) defined active and passive fatigue states that reflect different styles of workload regulation. In 2 driving simulator studies we investigated the multidimensional subjective states and safety outcomes associated with active and passive fatigue. Wind gusts were used to induce active fatigue, and full vehicle automation to induce passive fatigue. Drive duration was independently manipulated to track the development of fatigue states over time. Participants were undergraduate students. Study 1 (N = 108) focused on subjective response and associated cognitive stress processes, while Study 2 (N = 168) tested fatigue effects on vehicle control and alertness. In both studies the 2 fatigue manipulations produced different patterns of subjective response reflecting different styles of workload regulation, appraisal, and coping. Active fatigue was associated with distress, overload, and heightened coping efforts, whereas passive fatigue corresponded to large-magnitude declines in task engagement, cognitive underload, and reduced challenge appraisal. Study 2 showed that only passive fatigue reduced alertness, operationalized as speed of braking and steering responses to an emergency event. Passive fatigue also increased crash probability, but did not affect a measure of vehicle control. Findings support theories that see fatigue as an outcome of strategies for managing workload. The distinction between active and passive fatigue is important for assessment of fatigue and for evaluating automated driving systems which may induce dangerous levels of passive fatigue. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
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A driving simulator study compared the effect of changes in workload on performance in manual and highly automated driving. Changes in driver state were also observed by examining variations in blink patterns. With the addition of a greater number of advanced driver assistance systems in vehicles, the driver's role is likely to alter in the future from an operator in manual driving to a supervisor of highly automated cars. Understanding the implications of such advancements on drivers and road safety is important. A total of 50 participants were recruited for this study and drove the simulator in both manual and highly automated mode. As well as comparing the effect of adjustments in driving-related workload on performance, the effect of a secondary Twenty Questions Task was also investigated. In the absence of the secondary task, drivers' response to critical incidents was similar in manual and highly automated driving conditions. The worst performance was observed when drivers were required to regain control of driving in the automated mode while distracted by the secondary task. Blink frequency patterns were more consistent for manual than automated driving but were generally suppressed during conditions of high workload. Highly automated driving did not have a deleterious effect on driver performance, when attention was not diverted to the distracting secondary task. As the number of systems implemented in cars increases, an understanding of the implications of such automation on drivers' situation awareness, workload, and ability to remain engaged with the driving task is important.
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The study was designed to show how driver attention to the road scene and engagement of a choice of secondary tasks are affected by the level of automation provided to assist or take over the basic task of vehicle control. It was also designed to investigate the difference between support in longitudinal control and support in lateral control. There is comparatively little literature on the implications of automation for drivers' engagement in the driving task and for their willingness to engage in non-driving-related activities. A study was carried out on a high-level driving simulator in which drivers experienced three levels of automation: manual driving, semiautomated driving with either longitudinal or lateral control provided, and highly automated driving with both longitudinal and lateral control provided. Drivers were free to pay attention to the roadway and traffic or to engage in a range of entertainment and grooming tasks. Engagement in the nondriving tasks increased from manual to semiautomated driving and increased further with highly automated driving. There were substantial differences in attention to the road and traffic between the two types of semiautomated driving. The literature on automation and the various task analyses of driving do not currently help to explain the effects that were found. Lateral support and longitudinal support may be the same in terms of levels of automation but appear to be regarded rather differently by drivers.
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A driving simulator was used to assess the impact on fatigue, stress, and workload of full vehicle automation that was initiated by the driver. Previous studies have shown that mandatory use of full automation induces a state of "passive fatigue" associated with loss of alertness. By contrast, voluntary use of automation may enhance the driver's perceptions of control and ability to manage fatigue. Participants were assigned to one of two experimental conditions, automation optional (AO) and nonautomation (NA), and then performed a 35 min, monotonous simulated drive. In the last 5 min, automation was unavailable and drivers were required to respond to an emergency event. Subjective state and workload were evaluated before and after the drive. Making automation available to the driver failed to alleviate fatigue and stress states induced by driving in monotonous conditions. Drivers who were fatigued prior to the drive were more likely to choose to use automation, but automation use increased distress, especially in fatigue-prone drivers. Drivers in the AO condition were slower to initiate steering responses to the emergency event, suggesting optional automation may be distracting. Optional, driver-controlled automation appears to pose the same dangers to task engagement and alertness as externally initiated automation. Drivers of automated vehicles may be vulnerable to fatigue that persists when normal vehicle control is restored. It is important to evaluate automated systems' impact on driver fatigue, to seek design solutions to the issue of maintaining driver engagement, and to address the vulnerabilities of fatigue-prone drivers.
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This book presents an exhaustive review and evaluation of contemporary theoretical perspectives on SA and of a range of SA measurement approaches. A novel theory of DSA in complex sociotechnical systems is presented, followed by an original methodology for assessing SA and DSA in command and control environments. It contains several naturalistic case studies of command and control scenarios undertaken in numerous military domains, as well as one involving multiple high-consequence civilian domains. © Paul M. Salmon, Neville A. Stanton, Guy H. Walker and Daniel P. Jenkins 2009. All rights reserved.
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Driver drowsiness is a contributing factor in a significant number of single and multiple vehicle crashes. It is estimated that driver drowsiness accounts for around 20 to 30 percent of all traffic crashes in Australia, at a huge cost to society. A promising countermeasure designed to reduce the incidence of drowsiness-related crashes is a system that can detect drowsiness and issue warnings accordingly. Sleep Diagnostics, Pty Ltd, have developed an in-vehicle drowsiness detection and warning system called Optalert. The Monash University Accident Research Centre (MUARC) was approached by Sleep Diagnostics to conduct an evaluation of the Optalert system (glasses version 5, software version 4.1.5, algorithm version 08075) in terms of its effectiveness in predicting a breakdown in driving performance, using the MUARC advanced driving simulator.
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The central hypothesis of the work is that the dimension of control-no control plays an important role in motion sickness. Although it is generally agreed that having control over a moving vehicle greatly reduces the likelihood of motion sickness, few studies have addressed this issue directly, and the theoretical explanation for this phenomenon is not completely clear. In this study, we equated groups differing in controllability for head movement, vision, activity, and predictability, which have often been suggested in the literature as explanations for the driver's immunity to motion sickness. Twenty-two pairs of yoked subjects were exposed to nauseogenic rotation. One subject of each pair had control over the rotation and head movements, while the other was exposed passively to the same motion stimulus. Subjects who had control reported significantly fewer motion sickness symptoms and less of a decrement in their well-being, as compared to the yoked subject without control. The results are discussed in relation to Reason's sensory rearrangement theory and the concept of feed-forward mechanisms in motion perception.
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Future cars will be able to execute the longitudinal and lateral control and other subtasks of driving. Automation effects, known in other domains like aviation, rail traffic or manufacturing, will emerge in road transportation with consequences hard to predict from the present point of view. This paper discusses the current state of automation research in road traffic, concerning the take-over at system limits. Measurements like the take-over time and the maximum accelerations are suggested and substantiated with data from different experiments and literature. Furthermore, the procedure of such take-over situations is defined in a generic way. Based on studies and experience, advice is given concerning methods and lessons learned in designing and conducting take-over studies in driving simulation. This includes the test and scenario design and which dependent variables to use as metrics. Detailed information is given on how to generate proper control conditions by driving manually without automation. Core themes like how to keep situation presentation constant even for manual drivers and which measures to use to compare a take-over to manual driving are addressed. Finally, a prospect is given on further needs for research and limitations of current known studies.
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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.
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Despite being an accepted construct in traffic and transport psychology, the precise nature of behavioural adaptation, including its causes and consequences, has not yet been established within the road safety community. A comprehensive collection of recent literature, Behavioural Adaptation and Road Safety: Theory, Evidence, and Action explores behavioural adaptation in road users. It examines behavioural adaptation within the context of historical and theoretical perspectives, and puts forth tangible—and practical—solutions that can effectively address adverse behavioural adaptation to road safety interventions before it occurs. Edited by Christina Rudin-Brown and Samantha Jamson, with chapters authored by leading road safety experts in driver psychology and behaviour, the book introduces the concept of behavioural adaptation and details its more relevant issues. It reviews the definition of behavioural adaptation that was put forward by the OECD in 1990 and then puts this definition through its paces, identifying where it may be lacking and how it might be improved. This sets the context for the remaining chapters which take the OECD definition as their starting points. The book discusses the various theories and models of behavioural adaptation and more general theories of driver behaviour developed during the last half century. It provides examples of the "evidence" for behavioural adaptation—instances in which behavioural adaptation arose as a consequence of the introduction of safety countermeasures. The book then focuses on the internal, "human" element and considers countermeasures that might be used to limit the development of behavioural adaptation in various road user groups. The book concludes with practical tools and methodologies to address behavioural adaptation in research and design, and to limit the potential negative effects before they happen. Supplying easy-to-understand, accessible solutions that can be implemented early on in a road safety intervention’s design or conception phase, the chapters represent the most extensive compilation of literature relating to behavioural adaptation and its consequences since the 1990 OECD report. The book brings together earlier theories of behavioural adaptation with more recent theories in the area and combines them with practical advice, methods, and tangible solutions that can minimise the potential negative impact of behavioural adaptation on road user safety and address it before it occurs. It is an essential component of any road safety library, and should be of particular relevance to researchers, practitioners, designers, and policymakers who are interested in maximizing safety while at the same time encouraging innovation and excellence in road transport-related design.
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Advanced Driver Assistance Systems (ADAS) provide warnings and in some cases autonomous actions to increase driver and passenger safety by combining sensor technologies and situation awareness. In the last 10 years progressed from prototype demonstrators to full product deployment in motor vehicles. Early ADAS examples include Lane Departure Warning (LDW) and Forward Collision Warning (FCW) systems have been developed to warn drivers of potentially dangerous situations. More recently, driver inattention systems have made their debut. These systems are tackling one of the major causes of fatalities on roads-drowsiness and distraction. This paper describes DSS, a driver inattention warning system which has been developed by Seeing Machines for commercial applications, with an initial focus on heavy vehicle fleet management applications. A case study reporting a year-long realworld deployment of DSS is presented. The study showed the effectiveness of the DSS technology in mitigating driver inattention in a sustained manner.
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We investigated whether collision avoidance systems (CASs) should present individual crash alerts in a multiple-conflict scenario or present only one alert in response to the first conflict. Secondary alerts may startle, confuse, or interfere with drivers' execution of an emergency maneuver. Fifty-one participants followed a pickup truck around a test track. Once the participant was visually distracted, a trailing sedan repositioned itself into the participant's blind spot while a box was dropped from the truck Participants received a forward collision warning (FCW) alert as the box landed. Twenty-six drivers swerved left in response to the box, encountering a lateral conflict with the adjacent sedan. Half of these 26 drivers received a lane-change merge (LCM) alert. Drivers who received both the FCW and LCM alerts were significantly faster at steering away from the lateral crash threat than the drivers who received only the FCW alert (1.70 s vs. 2.76 s, respectively). Drivers liked receiving the LCM alert, rated it to be useful, found it easy to understand (despite being presented after the FCW alert), and did not find it to be startling. Drivers who are familiar with CASs benefit from, and feel it is appropriate to generate, multiple alerts in a multiple-conflict scenario. The results may inform the design of CASs for connected and automated vehicles.
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In the present study, we tested to what extent highly automated convoy driving involving small spacing ("platooning") may affect time headway (THW) and standard deviation of lateral position (SDLP) during subsequent manual driving. Although many previous studies have reported beneficial effects of automated driving, some research has also highlighted potential drawbacks, such as increased speed and reduced THW during the activation of semiautomated driving systems. Here, we rather focused on the question of whether switching from automated to manual driving may produce unwanted carryover effects on safety-relevant driving performance. We utilized a pre-post simulator design to measure THW and SDLP after highly automated driving and compared the data with those for a control group (manual driving throughout). Our data revealed that THW was reduced and SDLP increased after leaving the automation mode. A closer inspection of the data suggested that specifically the effect on THW is likely due to sensory and/or cognitive adaptation processes. Behavioral adaptation effects need to be taken into account in future implementations of automated convoy systems. Potential application areas of this research comprise automated freight traffic (truck convoys) and the design of driver assistance systems in general. Potential countermeasures against following at short distance as behavioral adaptation should be considered.
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Driver loss of vigilance is an important cause of road fatalities. The improvement of technologies makes now possible the implementation of in-vehicle driver monitoring systems assessing in real time the evolution of the driver state. Within this paper a Driver Vigilance Monitoring (DVM) system developed by Continental Automotive is described. This system includes a compact CMOS camera for observing the driver eyelid movements and a set of algorithms for analyzing in real time the image provided by the camera, to classify this information and at last to provide drowsiness diagnostic.
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The EU-funded project HAVEit aims at the realization of highly automated driving for intelligent transport. Within the Joint System approach in HAVEit automation is adapted to the intentions and limits of both of the two members in a Joint system- the driver and a technical co- system. In order to evaluate driver's performance capabilities it is necessary to online monitor his/her alertness and attention level. If the driver is detected as either drowsy or distracted it has to be decided how to bring him/her back into the loop by selecting the appropriate automation level. This paper presents an approach for online assessment of driver's state by using a combination of direct measures (e.g. eye lid measurement) and indirect measures (e.g. driving performance measures). A differentiated set of parameters is described that reflects the underlying energetic and attentional processes within the evolution of driver's state.
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After being widely applied in aviation, automation is increasingly applied to surface transportation. Furthermore, with the increased reliability and reduced cost of electronics and communications, it is becoming viable to develop a safe and reliable platooning system. These intelligent systems of the future will contribute to improved safety, efficiency, and journey time of vehicles while at the same time reducing stress for passengers. However, although new technologies make vehicle platooning possible, these new technologies will require interaction with drivers. Therefore, the development of appropriate Human -Machine Interfaces (HMI) progressively assumes greater importance, as diverse and innovative technologies are designed and implemented in vehicles. As a result of this interaction there is a need to research human aspects and the HMI. The main objective of this study consists of analyzing human aspects involved in vehicle platooning. Accordingly, this paper describes the human factors issues that come into play when introducing autonomous driving. A further study objective is to develop a high-quality HMI, and assess the effectiveness of the HMI, including the acceptability level from possible end-users point of view. This study is part of the European project "Safe Road Trains for the Environment, SARTRE", that aims to define several platoon requirements attributable to the driver's opinion, as well as to define the necessities to develop an appropriate HMI for a platooning environment. This takes into account information coming from objective parameters, logged during the simulation tests, and the driver preferences derived from acceptability assessment.
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The present paper describes a study that aims at assessment of driver behaviour in response to new technology, particularly Adaptive Cruise Control Systems (ACCs), as a function of driving style. In this study possible benefits and drawbacks of Adaptive Cruise Control Systems (ACCs) were assessed by having participants drive in a simulator. The four groups of participants taking part differed on reported driving styles concerning Speed (driving fast) and Focus (the ability to ignore distractions), and drove in ways which were consistent with these opinions. The results show behavioural adaptation with an ACC in terms of higher speed, smaller minimum time headway and larger brake force. Driving style group made little difference to these behavioural adaptations. Most drivers evaluated the ACC system very positively, but the undesirable behavioural adaptations observed should encourage caution about the potential safety of such systems.
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This test-track study assessed whether adaptive cruise control (ACC) induces behavioural adaptation in drivers. Eighteen experienced drivers drove a test vehicle while following a lead vehicle in three counterbalanced conditions: No ACC (self-maintained average headway of 2 s), ACC-Short (headway of 1.4 s) and ACC-Long (headway of 2.4 s). Results demonstrate that ACC can induce behavioural adaptation in drivers in potentially safety-critical ways. Compared to driving unsupported, participants located significantly more items per minute on a secondary task when using ACC, while their response times to a hazard detection task increased. This effect was particularly pronounced in those scoring high on a sensation-seeking scale. Using ACC resulted in significantly more lane position variability, an effect that was also more pronounced in high sensation-seekers. Drivers' trust in ACC increased significantly after using the system, and these ratings did not change despite a simulated failure of the ACC system during the ACC-Long condition. Response time to the simulated ACC failure was related to a driver's locus of control: Externals intervened more slowly than Internals. All drivers reported relying on the ACC system to keep their vehicle at a safe distance from the lead vehicle. Results are consistent with similar research conducted on lane departure warning systems. Driver awareness training is a potential preventive strategy that could minimize the behavioural adaptation associated with novel in-vehicle systems such as ACC.
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This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to over reliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by managers without due regard for the consequences for human performance, tends to define the operator's roles as by-products of the automation. Automation abuse can also promote misuse and disuse of automation by human operators. Understanding the factors associated with each of these aspects of human use of automation can lead to improved system design, effective training methods, and judicious policies and procedures involving automation use.
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Cars offer an excellent example of ubiquitous computing, and a technological revolution is currently underway that will eventually see in-vehicle computers empowered with increasingly complex sections of the driving task. In this article, we critically review the effect of ubiquitous computing in cars with reference to the psychology of the driver and present a survey of automotive researchers drawn from five major carmakers. The results illustrate the role of the computer in vehicles over the short, medium, and long term. Systems that are likely to be fitted into vehicles in the next 5 years include sophisticated electronic architectures and greater penetration of navigation and telematics systems. In the next 5 to 15 years drive by wire and collision sensing are anticipated. In the long term, 15 years and beyond, advanced driver-assistance systems will increasingly automate the driving task, and in-car personal computers and Internet will be commonplace. We conclude that the increased complexity and prominence of computing in cars requires further investigation of the needs, abilities, and limitations of the driver if the aims of safety, efficiency, and enjoyment, as well as greater ubiquity, are to be realized.
Article
As automated controllers supplant human intervention in controlling complex systems, the operators' role often changes from that of an active controller to that of a supervisory controller. Acting as supervisors, operators can choose between automatic and manual control. Improperly allocating function between automatic and manual control can have negative consequences for the performance of a system. Previous research suggests that the decision to perform the job manually or automatically depends, in part, upon the trust the operators invest in the automatic controllers. This paper reports an experiment to characterize the changes in operators' trust during an interaction with a semi-automatic pasteurization plant, and investigates the relationship between changes in operators' control strategies and trust. A regression model identifies the causes of changes in trust, and a 'trust transfer function' is developed using time series analysis to describe the dynamics of trust. Based on a detailed analysis of operators' strategies in response to system faults we suggest a model for the choice between manual and automatic control, based on trust in automatic controllers and self-confidence in the ability to control the system manually.
Article
Automation, in terms of systems such as adaptive/active cruise control (ACC) or collision warning systems, is increasingly becoming a part of everyday driving. These systems are not perfect though, and the driver has to be prepared to reclaim control in situations very similar to those the system easily handles by itself. This paper uses a questionnaire answered by 130 ACC users to discuss future research needs in the area of driver assistance systems. Results show that the longer drivers use their systems, the more aware of its limitations they become. Moreover, the drivers report that ACC forces them to take control intermittently. According to theory, this might actually be better than a more perfect system, as it provides preparation for unexpected situations requiring the driver to reclaim control.
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Recent advances in technology have meant that an increasing number of vehicle driving tasks are becoming automated. Such automation poses new problems for the ergonomist. Of particular concern in this paper are the twofold effects of automation on mental workload - novel technologies could increase attentional demand and workload, alternatively one could argue that fewer driving tasks will lead to the problem of reduced attentional demand and driver underload. A brief review of previous research is presented, followed by an overview of current research taking place in the Southampton Driving Simulator. Early results suggest that automation does reduce workload, and that underload is indeed a problem, with a significant proportion of drivers unable to effectively reclaim control of the vehicle in an automation failure scenario. Ultimately, this research and a subsequent program of studies will be interpreted within the framework of a recently proposed theory of action, with a view to maximizing both theoretical and applied benefits of this domain.