Exploring driving anger-caused impairment of takeover performance among professional taxi drivers during partially automated driving.

Partially automated systems are expected to reduce road crashes related to human error, even amongst professional drivers. Consequently, the applications of these systems into the taxi industry would potentially improve transportation safety. However, taxi drivers are prone to experiencing driving anger, which may subsequently affect their takeover performance. In this research, we explored how driving anger emotion affects taxi drivers' driving performance in various takeover scenarios, namely Mandatory Automation-Initiated transition (MAIT), Mandatory Driver-Initiated transition (MDIT), and Optional Driver-Initiated transition (ODIT). Forty-seven taxi drivers participated in this 2·3 mixed design simulator experiment (between-subjects: anger vs. calmness; within-subjects: MAIT vs. MDIT vs. ODIT). Compared to calmness, driving anger emotion led to a narrower field of attention (e.g., smaller standard deviations of horizontal fixation points position) and worse hazard perception (e.g., longer saccade latency, smaller amplitude of skin conductance responses), which resulted in longer takeover time and inferior vehicle control stability (e.g., higher standard deviations of lateral position) in MAIT and MDIT scenarios. Angry taxi drivers were more likely to deactivate vehicle automation and take over the vehicle in a more aggressive manner (e.g., higher maximal resulting acceleration, refusing to yield to other road users) in ODIT scenarios. The findings will contribute to addressing the safety concerns related to driving anger among professional taxi drivers and promote the widespread acceptance and integration of partially automated systems within the taxi industry.

Age-related differences in takeover performance: A comparative analysis of older and younger drivers in prolonged partially automated driving.

OBJECTIVE: Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or "takeover" control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. METHOD: A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). RESULTS: Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). CONCLUSIONS: These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.

Effects of a color gradient and emoji in AR-HUD warning interfaces in autonomous vehicles on takeover performance and driver emotions.

OBJECTIVE: This study examined the effects of color gradients and emojis in an augmented reality-head-up display (AR-HUD) warning interface on driver emotions and takeover performance. METHODS: A total of 48 participants were grouped into four different warning interfaces for a simulated self-driving takeover experiment. Two-way analysis of variance and the Kruskal-Wallis test was used to analyze takeover time, mood, task load, and system availability. RESULTS: Takeover efficiency and task load did not significantly differ among the interfaces, but the interfaces with a color gradient and emoji positively affected drivers' emotions. Emojis also positively affected emotional valence, and the color gradient had a high emotional arousal effect. Both the color gradient and the emoji interfaces had an inhibitory effect on negative emotions. The emoji interface was easier to learn, reducing driver learning costs. CONCLUSIONS: These findings offer valuable insights for designing safer and more user-friendly AR-HUD interfaces for self-driving cars.

The effects of various auditory takeover requests: A simulated driving study considering the modality of non-driving-related tasks.

With the era of automated driving approaching, designing an effective auditory takeover request (TOR) is critical to ensure automated driving safety. The present study investigated the effects of speech-based (speech and spearcon) and non-speech-based (earcon and auditory icon) TORs on takeover performance and subjective preferences. The potential impact of the non-driving-related task (NDRT) modality on auditory TORs was considered. Thirty-two participants were recruited in the present study and assigned to two groups, with one group performing the visual N-back task and another performing the auditory N-back task during automated driving. They were required to complete four simulated driving blocks corresponding to four auditory TOR types. The earcon TOR was found to be the most suitable for alerting drivers to return to the control loop because of its advantageous takeover time, lane change time, and minimum time to collision. Although participants preferred the speech TOR, it led to relatively poor takeover performance. In addition, the auditory NDRT was found to have a detrimental impact on auditory TORs. When drivers were engaged in the auditory NDRT, the takeover time and lane change time advantages of earcon TORs no longer existed. These findings highlight the importance of considering the influence of auditory NDRTs when designing an auditory takeover interface. The present study also has some practical implications for researchers and designers when designing an auditory takeover system in automated vehicles.

Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving.

In the context of high-level driving automation (SAE levels 4-5), several studies have shown that a personalized automated driving style, i.e., mimicking that of the human behind the wheel, can improve his experience. The objective of this simulator study was to examine the potential transfer of these benefits in the context of intermediate-level driving automation (SAE levels 2-3), focusing on driving speed personalization. In the first phase of the study, the driving speed of 52 participants was recorded. In the second phase, the same participants were driven by an automated car on a highway twice, and sometimes had to takeover during the drive because of a stationary vehicle on the lane. On these two drives, the automated car drove either at the same speed as them (personalized) or 20 km/h faster. The results showed that using a personalized speed driving style led to higher comfort, and that this effect was fully mediated by automated driving perceived safety. Although driving speed predicted automated driving perceived safety, this effect was actually moderated by trust in automated cars. Regarding takeover performance, the results showed that the brake use and maximum force were lower with the personalized speed driving style, leading to lower resulting maximum negative longitudinal acceleration and speed variability. Overall, the results of this study suggest that the benefits of automated driving style personalization in terms of speed extend to SAE levels 2-3. In addition to the experience benefits, this personalization approach could also improve traffic flow and safety.

Effects of driving style on takeover performance during automated driving: Under the influence of warning system factors.

Driving style has been proposed to be a critical factor in automated driving. However, the role of driving style in the process of taking over during automated driving needs further investigation. The main purpose of this study was to investigate the influence of driving style on takeover performance under the influence of warning system factors. In addition, this study also explored whether the impact of driving style on reaction time varies over time and the role of driving style on a comprehensive takeover quality indicator. Two driving simulation experiments with different takeover request (TOR) designs were conducted. In experiment 1, content warning information was provided in the TOR with different warning stage designs; in experiment 2, countdown warning information was provided in the TOR with different warning stage designs. Sixty-four participants (32 for experiment 1 and 32 for experiment 2) were classified into two groups based on their driving style (i.e., aggressive, or defensive) using the Chinese version of the Multidimensional Driving Style Inventory (the brief MDSI-C). The results suggested that drivers' driving style had significant effects on takeover performance, but the effects were influenced by warning system designs. Specifically, defensive participants performed better takeover performance, i.e., shorter reaction time and cautious vehicle control behaviors, than aggressive participants in most warning conditions. The content and countdown warning information and warning stage design affected the roles of driving style on takeover performance: 1) compared to the one-stage warning design, the two-stage warning design significantly shortened the reaction time of the participants with different driving styles, 2) compared to the countdown warning information design, the design of content warning information can shorten the reaction time of aggressive participants and lengthen the reaction time of defensive participants in the two-stage warning conditions, and 3) compared to the content warning information design, countdown warning information can improve the safe takeover performance of defensive participants. This study provides a better understanding of the role of driving style on takeover performance, and driving style should be considered when designing warning systems for autonomous vehicles.

PADA-AD Test - a standardized test method for assessing takeover performance of drivers during automated driving.

The presented method describes a standardized test procedure for the evaluation of takeover performance of drivers during automated driving. It was primarily developed to be used for evaluating Level 3 systems (conditional automated driving). It should be applied in a driving simulator environment during the development phase of a system. The method consists of a test course on a three-lane highway with 12 test scenarios where the driver repeatedly has to take over control from the automated system. Each scenario requires the driver to build up an adequate situation awareness in order to take the decision for the correct action. The method was explicitly designed to map the four relevant steps in the takeover process of automated driving: Perception - Awareness - Decision - Action and is therefore called PADA-AD Test for automated driving. The method description contains guidelines with regard to the specification of the test course and the included test scenarios, the design and duration of the automated drives, the non-driving related task to be performed during the automated drives, the instructions to be given to the subjects and finally the measures for evaluating takeover performance of the drivers. • A test procedure for the evaluation of takeover performance of drivers during automated driving was developed for usage in a driving simulator during the development phase of a system/HMI •The test course enables the assessment of the driver's takeover performance in various test scenarios including higher cognitive processes•The method is highly standardized and thus replicable through use of a predetermined test course with clearly defined scenarios, reduced environmental conditions and "popping up" of situational elements.

Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving.

The objective of this study is to examine the effects of visibility and time headway on the takeover performance in L3 automated driving. Both non-critical and critical driving scenarios were considered by changing the acceleration value of the leading vehicle. A driving simulator experiment with 18 driving scenarios was conducted and 30 participants complete the experiment. Based on the data obtained from the experiment, the takeover reaction time, takeover control time, and takeover responses were analyzed. The minimum Time-To-Collision (Min TTC) was used to measure the takeover risk level and a binary logit model for takeover risk levels was estimated. The results indicate that the visibility distance (VD) has no significant effects on the takeover control time, while the time headway (THW) and the acceleration of the leading vehicle (ALV) could affect the takeover control time significantly; most of the participants would push the gas pedal to accelerate the ego vehicle as the takeover response under non-critical scenarios, while braking was the dominant takeover response for participants in critical driving scenarios; decreasing the TCT and taking the appropriate takeover response would reduce the takeover risk significantly, so it is suggested that the automation system should provide the driver with the urgency of the situation ahead and the tips for takeover responses by audio prompts or the head-up display. This study is expected to facilitate the overall understanding of the effects of visibility and time headway on the takeover performance in conditionally automated driving.

The impact of different AR-HUD virtual warning interfaces on the takeover performance and visual characteristics of autonomous vehicles.

OBJECTIVE: The objective of this study was to determine the different effects of the arrow-pointing augmented reality head-up display (AR-HUD) interface, virtual shadow AR-HUD interface, and non-AR-HUD interface on autonomous vehicle takeover efficiency and driver eye movement characteristics in different driving scenarios. METHODS: Thirty-six participants were selected to carry out a simulated driving experiment, and the eye movement index and takeover time were analyzed. RESULTS: The arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface could effectively reduce the driver's visual distraction, improve the efficiency of obtaining visual information, reduce the number of times the driver's eyes leave the road, and improve the efficiency of the takeover compared with the non-AR-HUD interface, but there was no significant difference in eye movement indexes between the arrow pointing AR-HUD interface and the more eye-catching virtual shadow AR-HUD interface. When specific scenarios were considered, it was found that in the scenario of emergency braking of the vehicle in front, the arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface had more advantages in takeover efficiency than the non-AR-HUD interface. However, in the scenarios of a rear vehicle overtaking the vehicle ahead and non-motor vehicles running red lights, there was no significant difference in takeover efficiency. For the non-motor vehicle invading the line, emergency U-turn of the vehicle in front, and pedestrian crossing scenarios, the virtual shadow AR-HUD interface had the highest takeover efficiency. CONCLUSIONS: These research results can help improve the active safety of autonomous vehicle AR-HUD interfaces.

Speech-based takeover requests in conditionally automated driving: Effects of different voices on the driver takeover performance.

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.

Optimal Time Intervals in Two-Stage Takeover Warning Systems With Insight Into the Drivers' Neuroticism Personality.

Conditional automated driving [level 3, Society of Automotive Engineers (SAE)] requires drivers to take over the vehicle when an automated system's failure occurs or is about to leave its operational design domain. Two-stage warning systems, which warn drivers in two steps, can be a promising method to guide drivers in preparing for the takeover. However, the proper time intervals of two-stage warning systems that allow drivers with different personalities to prepare for the takeover remain unclear. This study explored the optimal time intervals of two-stage warning systems with insights into the drivers' neuroticism personality. A total of 32 drivers were distributed into two groups according to their self-ratings in neuroticism (high vs. low). Each driver experienced takeover under the two-stage warning systems with four time intervals (i.e., 3, 5, 7, and 9 s). The takeover performance (i.e., hands-on-steering-wheel time, takeover time, and maximum resulting acceleration) and subjective opinions (i.e., appropriateness and usefulness) for time intervals and situation awareness (SA) were recorded. The results showed that drivers in the 5-s time interval had the best takeover preparation (fast hands-on steering wheel responses and sufficient SA). Furthermore, both the 5- and 7-s time intervals resulted in more rapid takeover reactions and were rated more appropriate and useful than the 3- and 9-s time intervals. In terms of personality, drivers with high neuroticism tended to take over immediately after receiving takeover messages, at the cost of SA deficiency. In contrast, drivers with low neuroticism responded safely by judging whether they gained enough SA. We concluded that the 5-s time interval was optimal for drivers in two-stage takeover warning systems. When considering personality, drivers with low neuroticism had no strict requirements for time intervals. However, the extended time intervals were favorable for drivers with high neuroticism in developing SA. The present findings have reference implications for designers and engineers to set the time intervals of two-stage warning systems according to the neuroticism personality of drivers.

A Wizard-of-Oz experimental approach to study the human factors of automated vehicles: Platform and methods evaluation.

OBJECTIVES: Driving simulation is an important platform for studying vehicle automation. There are different approaches to using this platform - with most using scripting or programmatic tools to simulate vehicle automation. A less frequently used approach, the Wizard-of-Oz method, has potential for increased flexibility and efficiency in designing and conducting experiments. This study designed and evaluated an experimental setup to examine the feasibility of this approach as an alternative for conducting automation studies. METHODS: Twenty-four participants experienced simulated vehicle automation in two platforms, one where the automation was controlled by algorithms, and the other where the automation was simulated by an external operator. Surveys were administered after each drive and the drivers' takeover performance after the automation disengaged was measured. RESULTS: Results indicate that while the kinematic parameters of the driving differed significantly for the two platforms, there were no significant differences in the perceptions of participants and in their takeover performance between the two platforms. CONCLUSION: These results provide evidence for the use of alternative approaches for the conduct of human factors studies on vehicle automation, potentially lowering barriers to undertaking such experiments while increasing flexibility in designing more complex studies.

Promote or inhibit: An inverted U-shaped effect of workload on driver takeover performance.

OBJECTIVE: In conditional automated driving (SAE Level 3), drivers are required to take over their vehicles when the automated systems fail. Non-driving related tasks (NDRTs) can positively or negatively affect takeover safety, but the underlying reasons for this inconsistency remain unclear. This study aims to investigate how various workload levels generated by NDRTs may influence the takeover performance of drivers and the lead time they require. METHOD: Fifty drivers were randomly distributed into five groups, which corresponded to five workload levels (1-4 levels generated by Tetris game; control level generated by monitoring). Each driver completed vehicle takeover tasks upon receiving takeover requests with various lead times (3, 5, 7, 9, and 11 s) while engaging in NDRTs. The drivers' takeover performance and subjective opinions were recorded. RESULTS: Drivers in the moderate workload condition (i.e., level 3) had significantly shorter takeover times and better takeover quality than those in the lower (i.e., level 1 and level 2) or higher (i.e., level 4) workload conditions. They also subjectively required less lead time in the moderate condition. Moreover, the drivers rated 7 s as the most appropriate lead time despite the improvement in their overall takeover performances with increased lead time. CONCLUSIONS: This study found an inverted U-shaped relationship between the drivers' workload generated by NDRTs and takeover performance. The moderate workload level (rather than the lower or higher workload level) led to a faster and better takeover performance, and it seemed to require minimal lead time for drivers. These findings help understand the relationship of drivers' workload during the automation and takeover performance in conditional automated driving. An important recommendation emerging from this work is to investigate what should be the most efficient method to detect the drivers' workload state real-time and give feedback to them when it comes to overload or underload during the automated driving.

Sleep in highly automated driving: Takeover performance after waking up.

Takeover performance in automated driving is subject to investigation in the context of a variety of driver states such as distraction or drowsiness. New driver states will emerge with increasing automation level with drivers potentially being allowed to sleep while driving a highly automated vehicle. Still at some point during a drive, drivers will be required to or voluntarily take back control of the vehicle. A simulator study was conducted to investigate drivers' ability to take over the vehicle control after sleeping. In a within-subjects study design N = 25 test drivers completed a drive using a highly automated driving system a) during day time after a full night of sleep and b) early in the morning after a night of partial sleep deprivation. During the second drive, sleep was measured in drivers according to the American Academy of Sleep Medicine (AASM) standard using electroencephalography (EEG). In total, the participants had to handle four takeover requests (TORs) from the system, two while being awake (day drive) and two when being awakened from sleep stage N2 (morning drive). The objective criticality of the situations was assessed performing the Takeover Controllability rating (TOC-rating). The results indicate that the applied takeover time of 60 s was sufficient for drivers to reengage in driving after sleeping. Reaction times were extended by about 3 s after sleep compared to the wake condition. Takeover performance assessed with the TOC-rating however was clearly worse after sleep than after wakefulness which was also reflected in the drivers' subjective perception of the criticality of the situation. Further research is needed on how to deal with performance impairments after waking up from sleep during automated driving.

Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non-Driving Related Tasks on Takeover Time and Takeover Quality.

OBJECTIVE: This study aimed at investigating the driver's takeover performance when switching from working on different non-driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. BACKGROUND: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased. METHOD: Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart. RESULTS: Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver's lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly "harmless." CONCLUSION: Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload. APPLICATION: The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.