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Objective
The effectiveness of three types of in-vehicle warnings was assessed in a driving simulator across different noise conditions.
Background
Although there has been much research comparing different types of warnings in auditory displays and interfaces, many of these investigations have been conducted in quiet laboratory environments with...
Context in source publication
Context 1
... main effect of background noise type was not significant. No 334 significant interacion was found between the two factors (see Figure 3). Therefore, we conducted separate non-parametric tests for both the within-subjects factor 360 (background noise type; Friedman test) and the between-subjects factor (auditory warning type; ...
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Testing of vehicle design properties by car manufacturers is primarily performed on-road and is resource-intensive, involving costly physical prototypes and large time durations between evaluations of alternative designs. In this paper, the applicability of driving simulators for the virtual assessment of ride, steering and handling qualities was s...
Citations
... This was because drivers' heads and eyes were not oriented toward the visual displays during automated driving. Sabi c et al. (2021) also revealed that the background noise in the vehicle could impair the effectiveness of auditory warnings. By contrast, the tactile TOR could handle these issues. ...
The vibrotactile modality has great potential for presenting takeover requests (TORs) to get distracted drivers back into the control loop. However, few studies investigate the effectiveness of directional vibrotactile TORs. Whether TORs should be directed toward the direction of hazard (stimulus-response incompatibility) or the direction of avoidance action (stimulus-response compatibility) remains inconclusive. The present study explored the impact of directional vibrotactile TORs (toward-hazard, toward-action, and non-directional) on takeover performance. The influences of TORs lead time (3 s, 4 s, 6 s, and 8 s) and non-driving related tasks (NDRTs) (playing Tetris games and monitoring the road) on the effect of directional TORs were also probed. A total of 48 participants were recruited for our simulated driving study. Results showed that when drivers were engaged in NDRTs during automated driving, directional TORs were more effective than non-directional TORs. Specifically, at the lead times of 6 s and 8 s, both toward-hazard and toward-action TORs could shorten steering response times, compared with the non-directional TORs. At the lead times of 3 s and 4 s, toward-action TORs were more beneficial, as the maximum lateral acceleration was smaller than toward-hazard and non-directional TORs. However, when drivers monitored the road during automated driving, no obvious difference existed between directional and non-directional TORs, regardless of how long the lead time was. The findings in the present study shed light on the design and implementation of the tactile takeover system for automobile designers.
... Compared to visual TORs, auditory and vibrotactile TORs are both more effective and result in significantly shorter takeover time (Petermeijer et al., 2017); however, vibrotactile TORs may cause drivers to perceive a higher level of annoyance and have poor user experience (Politis et al., 2015). Accordingly, auditory feedback is increasingly recommended and applied in current vehicles (e.g., Tesla, 2020), and it can take advantage of speech to convey detailed information effectively and has no problem with intrusiveness (Campbell et al., 2016;Edin et al., 2019). ...
... Considering the engagement in NDRTs and the loss of situational awareness in conditionally AD, a combination of non-verbal warnings and speech could provide a possible solution, which was also mentioned by some participants. However, in a study exploring auditory interfaces in manual driving, Edin et al. (2019) found that presenting an alerting tone prior to the speech or auditory icons did not improve the participant recognition of warnings. They thought that this might be a result of insufficient design of the alerting tone or inadequate training of the participants. ...
This study explored the possibility of applying personalized takeover requests (TORs) in an automated driving system (ADS), which required drivers to regain control when the system reached its limits. A driving simulator experiment was conducted to investigate how speech-based TOR voices impacted driver performance in takeover scenarios with two lead time conditions in conditionally automated driving (level 3). Eighteen participants drove in three sessions, with each session having a different TOR voice (a synthesized male voice, a synthesized female voice, and a significant other voice). Two scenarios with a lead time of 5 s and two scenarios with a lead time of 12 s were provided per session. The driver takeover time and quality data were collected. A follow-up interview was conducted to gain a clearer understanding of the drivers’ psychological feelings about each TOR voice during takeovers. Changes in takeover time and takeover quality caused by TOR voices were similar in both lead time conditions, except for the lateral acceleration. The synthesized male voice led to a larger maximum lateral acceleration than the other two voices in the 5 s condition. Interestingly, most drivers preferred choosing the synthesized female voice for future takeovers and showed negative attitudes toward the significant other voice. Our results implied that choosing TOR voices should consider the drivers’ daily voice-usage habits as well as specific context of use, and personalized TOR voices should be incorporated into the ADS prudently.
... Spearcons, speech-based earcons, were introduced by Walker et al. [61] for menu-based interfaces and are generated by "speeding up a spoken phrase until it is no longer recognized as speech"; spearcons have the advantage to be acoustically unique. Šabic et al. [44] found out (based on two experiments on in-vehicle warnings, N 1 =60, N 2 =60) that spearcons performed better than text-to-speech warnings in quiet environments and similar in noisier environments, but recommended using text-to-speech for safety-critical warnings in potentially noisier environments. ...
... Spearcons (speech-based earcons) were introduced by Walker et al. [42] for menu-based interfaces and are generated by "speeding up a spoken phrase until it is no longer recognized as speech"; spearcons have the advantage to be acoustically unique. Šabic et al. [43] found out (based on two experiments on in-vehicle warnings, N 1 = 60, N 2 = 60) that spearcons performed better than text-to-speech warnings in quiet environments and similar in noisier environments, but recommended using textto-speech for safety-critical warnings in potentially noisier environments. ...
Numerous statistics show that cyclists are often involved in road traffic accidents, often with serious outcomes. One potential hazard of cycling, especially in cities, is “dooring”—passing parked vehicles that still have occupants inside. These occupants could open the vehicle door unexpectedly in the cyclist’s path—requiring a quick evasive response by the cyclist to avoid a collision. Dooring can be very poorly anticipated; as a possible solution, we propose in this work a system that notifies the cyclist of opening doors based on a networked intelligent transportation infrastructure. In a user study with a bicycle simulator (N = 24), we examined the effects of three user interface designs compared to a baseline (no notifications) on cycling behavior (speed and lateral position), perceived safety, and ease of use. Awareness messages (either visual message, visual message + auditory icon, or visual + voice message) were displayed on a smart bicycle helmet at different times before passing a parked, still-occupied vehicle. Our participants found the notifications of potential hazards very easy to understand and appealing and felt that the alerts could help them navigate traffic more safely. Those concepts that (additionally) used auditory icons or voice messages were preferred. In addition, the lateral distance increased significantly when a potentially opening door was indicated. In these situations, cyclists were able to safely pass the parked vehicle without braking. In summary, we are convinced that notification systems, such as the one presented here, are an important component for increasing road safety, especially for vulnerable road users.
... The main factors affecting the value of traffic noise generated by road vehicles according to the Cnossos-EU model are: vehicle category and speed, interaction of tires with the road surface, traffic intensity, road parameters, and weather conditions. In addition, noise contributes to the mental burden on everyone on the road [21,22,23,24,25]. Each of these factors also affects the number of road accidents but to a different extent. ...
Autonomous driving will still use human-machine co-driving to handle complex situations for a long term, which requires the driver to control the vehicle and avoid hazards by executing appropriate behavioral sequences after takeover prompts. Previous studies focused on the division of static behavioral indicators and major phases in the initial phase of takeover, while lacking the construction of behavioral sequences based on the dynamic changes of behavioral characteristics during the takeover process. This study divides the takeover process in a detailed manner and investigates the impact of audio types on the behavioral sequence at each phase. 20 professional drivers performed the NDRT in autonomous driving mode on real roads, and after receiving audio prompts, they took over the vehicle and performed hazard avoidance maneuvers. The results show that the behavioral characteristics could construct the behavioral sequence of different phases, with the dynamic characteristics of the takeover operation change. In addition, different types of audio prompts will affect the timing of the takeover operation and its driving performance. Choosing different audio prompts or combinations can help improve the effect of taking over the vehicle. This study helps to provide guidance on the design of human-machine interaction for behavior optimization at different phases, so that guiding the driver to take over the vehicle safely and effectively.
Level crossing safety is a well-researched safety issue worldwide, but little attention has been placed on the safety benefits of using train horns when a train approaches a level crossing. Given train horns' adverse effects on the health and well-being of residents living near rail tracks, the use of train horns must be beneficial to safety. The current study sought to determine in a laboratory environment whether road users (N = 31) can detect the range of train horns observed in Australia in terms of loudness and duration, using high-definition audio recordings from railway crossings. A repeated measures design was used to evaluate the effects of key factors likely to influence the detectability of train horns, including, visual and auditory distractive tasks, hearing loss and environmental noise (crossing bells). Train horn detectability was assessed based on participants' accuracy and reaction times. Results indicated the duration of the train horn had the most influential effect on the detectability of train horns, with short-duration train horns less likely to be detected. The presence of bells at a crossing was the second most important factor that limited train horn detection. Train horn loudness also affected detectability: faint blasts were less likely to be noticed, while loudest blasts were more likely to be noticed. However, loud horns reduced the ability to detect the side from which the train was approaching and may result in longer times to detect the train, in the field. The auditory distractive task reduced the train horn detection accuracy and increased reaction time. However, the visual distractive task and medium to severe hearing loss were not found to affect train horn detection. This laboratory study is the first to provide a broad understanding of the factors that affect the detectability of Australian train horns by road users. The findings from this study provide important insights into ways to reduce the use and modify the practice to mitigate the negative effects of train horns while maintaining the safety of road users.
Autonomous vehicles (AVs) are expected to play an increasingly important role in future transportation systems as a promising means of improving road safety and efficiency by eventually replacing human-driven vehicles. Semi-autonomous vehicles (semi-AVs; SAE Level 2 and Level 3) feature automatic lateral and longitudinal control of the vehicle with human drivers required to supervise the system at all times (Level 2) or prepared to resume control when requested (Level 3). As these definitions reveal, semi-AVs still require human oversight and intervention to fully ensure safety. Humans are required to monitor and be ready to take over control when the vehicle fails to recognize or respond to hazardous events. Thus, it is essential to ensure effective human-automation interaction and collaboration for semi-AVs. This book chapter will discuss the critical challenges for effective human-automation interaction for semi-autonomous driving, including communicating potential risks to human drivers and maintaining proper driver trust in the semi-AV. Risks in the current context are moving or stationary objects and road environments that impose imminent threats to drivers, including overt hazards such as road obstacles, a pedestrian crossing the road, and an intruding vehicle, or covert hazards such as a pedestrian that is about to cross but is occluded by a parked truck or a roadway structure. We discuss the design of effective risk communication mechanisms to convey these risks to the human driver, which helps maintain the driver’s situation awareness and facilitate the driver’s actions when needed. In addition, the effectiveness of this risk communication can be influenced not only by the characteristics of the driver and the semi-AV, but also their interaction. Finally, we will discuss factors that affect drivers’ trust in semi-AVs and subsequently how it affects effective risk communication in semi-AV driving.KeywordsSemi-autonomous drivingTrustRisk communication
Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.
