On the impact of on-road partially-automated driving on drivers' cognitive workload and attention allocation

The use of partially-automated or SAE level-2 vehicles is expected to change the role of the human driver from operator to supervisor, which may have an effect on the driver's workload and visual attention. In this study, 30 Ontario drivers operated a vehicle in manual and partially-automated mode. Cognitive workload was measured by means of the Detection Response Task, and visual attention was measured by means of coding glances on and off the forward roadway. No difference in cognitive workload was found between driving modes. However, drivers spent less time glancing at the forward roadway, and more time glancing at the vehicle's touchscreen. These data add to our knowledge of how vehicle automation affects cognitive workload and attention allocation, and show potential safety risks associated with the adoption of partially-automated driving.

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.

The monitoring requests on young driver's fatigue and take-over performance in prolonged conditional automated driving

INTRODUCTION

L3 automated vehicles can perform all dynamic driving tasks unless a take-over occurs due to operational limits. This issue is potentially important for young drivers who are vulnerable road users since they have skill deficits and easily evolve into aberrant driving. However, drivers lacking active involvement may be fatigued and drowsy. Previous research indicated that performing a voluntary non-driving-related task (NDRT) could keep drivers alert, but there was no difference in take-over performance with or without NDRT. Providing a monitoring request (MR) before a possible take-over request (TOR) exhibited better take-over performance in temporary automated driving. Therefore, the study aimed to investigate the effects of MR and voluntary NDRT on young drivers' fatigue and performance.

METHOD

Twenty-five young drivers experienced 60 min automated driving on a highway with low traffic density and a TOR prompted due to a collision event. A within-subjects was designed that comprised three conditions: NONE, TOR-only, and MR + TOR. Drivers were allowed to perform a self-paced phone NDRT during automated driving.

RESULTS

The PERCLOS and blink frequency data showed that playing phones could keep drivers vigilant. The take-over performance on whether taking phone had no difference, but with MRs condition exhibited better take-over performance including the shorter reaction time and the longer TTC. Subjective evaluations also showed the advantages of MRs with more safety, trust, acceptance, and lower workload.

CONCLUSIONS

Taking MRs had a positive effect on relieving fatigue and improving take-over performance. Furthermore, MRs could potentially improve the safety and acceptance of automated driving.

PRACTICAL APPLICATIONS

The MR design can be used in the automotive industry to ensure the safest interfaces between fatigue drivers and automation systems.

Driver behavior and mental workload for takeover safety in automated driving: ACT-R prediction modeling approach

OBJECTIVE

Conditional automated driving (SAE level 3) requires the driver to take over the vehicle if the automated system fails. The mental workload that can occur in these takeover situations is an important human factor that can directly affect driver behavior and safety, so it is important to predict it. Therefore, this study introduces a method to predict mental workload during takeover situations in automated driving, using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. The mental workload prediction model proposed in this study is a computational model that can become the basis for emerging crash avoidance technologies in future autonomous driving situations.

METHODS

The methodology incorporates the ACT-R cognitive architecture, known for its robustness in modeling cognitive processes and predicting performance. The proposed takeover cognitive model includes the symbolic structure for repeatedly checking the driving situation and performing decision-making for takeover as well as Non-Driving-Related Tasks (NDRT). We employed the ACT-R cognitive model to predict mental workload during takeover in automated driving scenarios. The model's predictions are validated against physiological data and performance data from the validation test.

RESULTS

The model demonstrated high accuracy, with an r-square value of 0.97, indicating a strong correlation between the predicted and actual mental workload. It successfully captured the nuances of multitasking in driving scenarios, showcasing the model's adaptability in representing diverse cognitive demands during takeover.

CONCLUSIONS

The study confirms the efficacy of the ACT-R model in predicting mental workload for takeover scenarios in automated driving. It underscores the model's potential in improving driver-assistance systems, enhancing vehicle safety, and ensuring the efficient integration of human-machine roles. The research contributes significantly to the field of cognitive modeling, providing robust predictions and insights into human behavior in automated driving tasks.

Driver-initiated take-overs during critical evasion maneuvers in automated driving

The aim of the current study is to investigate predictors and consequences of driver-initiated take-overs during automated evasion maneuvers. Literature on control transitions in automated driving has mainly focused on system-initiated take-overs. However, drivers may also initiate take-overs without take-over requests. To date, such driver-initiated take-overs have rarely been investigated. Our study addresses this research gap. In a driving simulator study with 61 participants, we investigated whether the criticality of highly dynamic evasion maneuvers and trust in automation affect the probability of driver-initiated take-overs. Criticality was manipulated via time headway (THW) and traction usage (TU). Trust was varied by manipulating automation reliability before the experimental trials. Consequences of driver-initiated take-overs in terms of collisions and lane departures were assessed. The results indicate that THW, TU, and trust affect the probability of driver-initiated take-overs. Moreover, the time it takes the automation to respond to an obstacle ahead by starting an evasion maneuver may be another relevant factor in predicting take-overs. After a take-over, drivers produced a number of unnecessary lane departures and collisions. These were independent of THW and TU. The study demonstrates that drivers are more likely to take over vehicle control during automated evasion maneuvers without take-over requests when criticality increases and trust in automation decreases. Such take-overs may be hazardous for traffic safety. Our findings help to design automated vehicles that avoid unnecessary take-overs in critical driving situations or de-escalate their consequences effectively, thus increasing traffic safety.

Assessing the physiological effect of non-driving-related task performance and task modality in conditionally automated driving systems: A systematic review and meta-analysis

In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers' physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.

Anticipated fear and anxiety of Automated Driving Systems: Estimating the prevalence in a national representative survey

Background

Automated Driving Systems (ADS) may reshape mobility. Yet, related fear and anxiety are largely unknown. We estimated the prevalence and risk factors of anticipated anxiety towards ADS.

Method

In a nationally representative face-to-face household survey, we assessed anticipated levels of anxiety towards ADS based on DSM-5 specific phobia criteria, using structured diagnostic interviews. We estimated weighted prevalences and conducted adjusted logistic regression models.

Results

Of N = 2076 respondents, 40.82% (95%-confidence interval (CI) 37.73-43.98) anticipated experiencing some symptoms of phobia of ADS, 15.22% (CI 13.19-17.51) anticipated subthreshold phobia, and 3.39% (CI 2.42-4.75) anticipated full-blown phobia of ADS. Of subjects anticipating subthreshold phobia, 74.02% showed no strong, enduring fears of driving non-automated cars and 65.07% presented no other specific phobias (full-blown anticipated phobia: 50.37% and 50.03%, respectively). Anticipated phobia highly overlapped with anticipating marked or strong fears of passively encountering ADS in traffic (odds ratio 312.4-1982.2).

Conclusion

About 20% of subjects anticipated at least subthreshold and 4% of subjects anticipated full-blown phobia of ADS. It appears to be distinct from fears related to non-automated driving and other specific phobias. Our findings call for prevention and treatment of phobia of ADS as they become increasingly ubiquitous.

Impact of duration of monitoring before takeover request on takeover time with insights into eye tracking data

Safety has become the primary concern of automated driving system (ADS) in recent years. Compared with highly automated driving (L4 and above), conditionally automated driving (L3/L3+ ADS) seems to be a moderate choice, where drivers are required to respond to the takeover request (TOR) whenever necessary. It is the system's responsibility to make sure that the takeovers would be safe at the time of issuing the TOR. To realize that, a lot of factors need to be taken into consideration. As it has been found that drivers' eyes-on-road gazes increase slowly in the first few seconds while transferring to manual driving from automated driving and drivers' gaze behaviors are related with situation awareness, the main aim of this study is to investigate the impact of duration of monitoring before the TOR on takeover time and whether there is a positive or negative relationship between the two. To verify these, we designed a driving simulator study where the TOR was issued 0 s, 5 s, 10 s and ≥ 15 s after the non-driving-related task has ended. Twelve scenarios were designed, and the results from 36 participants showed that there was indeed a statistically significant difference, however, the relationship was neither positive nor negative, which was close to a parabola. Analyzing results of eye movements and gaze behavior further supported this conclusion. It is therefore concluded the duration of monitoring before the TOR should neither be too short nor too long, and 5-7 s would be appropriate choices. This is desirable not only for improving takeover performance of drivers but also for improving the prediction model for predicting takeover performance of drivers that has yet to be studied, so as to improve safety, reliability and acceptance of the ADS.

User preferences, driving context or manoeuvre characteristics? Exploring parameters affecting the acceptability of automated overtaking

Future user acceptance will be a requirement for the AVs to accomplish their estimated safety benefits, highlighting the importance of acceptable driving behaviour. This study aims to investigate the parameters that affect the acceptability of highly automated overtaking. 237 respondents participated in a video based online survey, rating different motorway flying overtaking scenarios based on their preferences. The scores were analysed using a variety of methods (statistical tests, Principal Component Analysis, Linear Mixed Models). Long pull-out distances and manoeuvre duration values, as well as lower speeds were preferred by the participants, with some limited impact of the driving situation. Overall, behaviour simulating an average, cautious human driver is likely to positively influence acceptability and suggests the value of further research on context-adaptive automated driving to account for subjective risk perception. These findings can contribute towards user-centred systems that assist or autonomously perform overtaking manoeuvres, supporting their uptake and thus the realisation of their safety benefits.

The interaction between perceived safety and perceived usefulness in automated parking as a result of safety distance

Improved safety and traffic efficiency are among the proclaimed benefits of automated driving functions. In many scenarios, traffic safety and efficiency can be somewhat contradictory, especially in the perception of a user. In order for potential users to accept the automated system, it is necessary to find the optimal system configuration. Therefore, it is important to understand how the factors underlying acceptance develop and interact. In this study, seven safety distances of an automated parking system were implemented resulting in parking manoeuvres of varying efficiency (in terms of required moves). Participants experienced each configuration twice and rated their perceived safety and perceived usefulness. The results show that maximizing safety distances results in high perceived safety, yet also a diminished perceived usefulness due to reduced efficiency. On the other hand, maximum efficiency leads to a lower perceived safety and thus, a reduced rating of perceived usefulness. Furthermore, in some participants, perceived safety increased gradually, while for others, a threshold effect could be observed. The results demonstrate that the specification of a sole system characteristic can have multiple effects. These have to be considered to maximize acceptance.

Repeated conditionally automated driving on the road: How do drivers leave the loop over time?

The goal of this on the road driving study was to investigate how drivers adapt their behavior when driving with conditional vehicle automation (SAE L3) on different occasions. Specifically, we focused on changes in how fast drivers took over control from automation and how their gaze off the road changed over time. On each of three consecutive days, 21 participants drove for 50 min, in a conditionally automated vehicle (Wizard of Oz methodology), on a typical German commuting highway. Over these rides the take-over behavior and gaze behavior were analyzed. The data show that drivers' reactions to non-critical, system initiated, take-overs took about 5.62 s and did not change within individual rides, but on average became 0.72 s faster over the three rides. After these self-paced take-over requests a final urgent take-over request was issued at the end of the third ride. In this scenario participants took over rapidly with an average of 5.28 s. This urgent take-over time was not found to be different from the self-paced take-over requests in the same ride. Regarding gaze behavior, participants' overall longest glance off the road and the percentage of time looked off the road increased within each ride, but stayed stable over the three rides. Taken together, our results suggest that drivers regularly leave the loop by gazing off the road, but multiple exposures to take-over situations in automated driving allow drivers to come back into loop faster.

Assessing the physiological effect of non-driving-related task performance in conditionally automated driving systems: A systematic review and meta-analysis protocol

Background

Level 3 automated driving systems involve the continuous performance of the driving task by artificial intelligence within set environmental conditions, such as a straight highway. The driver's role in Level 3 is to resume responsibility of the driving task in response to any departure from these conditions. As automation increases, a driver's attention may divert towards non-driving-related tasks (NDRTs), making transitions of control between the system and user more challenging. Safety features such as physiological monitoring thus become important with increasing vehicle automation. However, to date there has been no attempt to synthesise the evidence for the effect of NDRT engagement on drivers' physiological responses in Level 3 automation.

Methods

A comprehensive search of the electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO, and IEEE Explore will be conducted. Empirical studies assessing the effect of NDRT engagement on at least one physiological parameter during Level 3 automation, in comparison with a control group or baseline condition will be included. Screening will take place in two stages, and the process will be outlined within a PRISMA flow diagram. Relevant physiological data will be extracted from studies and analysed using a series of meta-analyses by outcome. A risk of bias assessment will also be completed on the sample.

Conclusion

This review will be the first to appraise the evidence for the physiological effect of NDRT engagement during Level 3 automation, and will have implications for future empirical research and the development of driver state monitoring systems.

Resilience engineering on the road: Using operator event sequence diagrams and system failure analysis to enhance cyclist and vehicle interactions

Future visions of transport systems include both a drive towards automated vehicles and the need for sustainable, active, modes of travel. The combination of these requirements needs careful consideration to ensure the integration of automated vehicles does not compromise vulnerable road users. Transport networks need to be resilient to automation integration, which requires foresight of possible challenges in their interaction with other road users. Focusing on a cyclist overtake scenario, the application of operator event sequence diagrams and a predictive systems failure method provide a novel way to analyse resilience. The approach offers the opportunity to review how automation can be positively integrated into road transportation to overcome the shortfalls of the current system by targeting organisational, procedural, equipment and training measures.

The driver's instantaneous situation awareness when the alarm rings during the take-over of vehicle control in automated driving

OBJECTIVE

The driver's instantaneous situation awareness in the process of take-over of vehicle control in automated driving has not yet been thoroughly investigated. The proposed research can provide a better understanding of the driver's perceived characteristics and identify the most urgent information requirements of the on-site scenario when the driver's eye sight returns from other distractors to the driving scene.

METHODS

We conducted an experiment in simulated automated driving to study the participants' ability of instantaneous hazard perception and judgment. The scene pictures, which were displayed in millisecond time, were used to imitate the situations that drivers would see when the distracted drivers returned their gaze from the distractive sources to the road in the simulated automated driving.

RESULTS

The results show that the driving state, scene representation time and hazard levels affect the instantaneous situation awareness of drivers. In addition, the scene perception accuracy of the group who played games during automated driving is much lower than that of the group that chatted with the copilot. The longer picture-showing duration decreases the accuracy of hazard identification, whereas the shorter picture-showing duration increases the accuracy of hazard perception and the hazard rating score.

CONCLUSIONS

In conclusion, distraction reduces the accuracy of the instantaneous scene perception of drivers, and drivers behave more cautiously in decision making when the driving situations are more hazardous. This study provides a good theoretical basis for the design of hazard warning information for automated driving.

Driver-initiated take-overs during critical braking maneuvers in automated driving - The role of time headway, traction usage, and trust in automation

Transitions of vehicle control between automated vehicle and driver remain a necessity in the near future. Most research focuses on system-initiated transitions of control. However, drivers may also actively decide to take over without being prompted by the automation. The present study aims to uncover predictors of such driver-initiated take-overs in automated driving and to examine their impact on traffic safety. We conducted two driving simulator studies with a total of 100 participants examining whether trust in automation and the criticality of the driving situation predict driver-initiated take-overs during highly dynamic braking maneuvers. Trust was varied via automation reliability in a prior induction phase, while criticality was manipulated via different levels of time headway (THW) and traction usage (TU). Potential limitations of study 1 concerning trust induction and predictor operationalization were addressed and eliminated in study 2. Results of both studies show that drivers' trust in automation and THW affected the probability of driver-initiated take-overs. TU affected take-over probability only in interaction with THW and trust. Moreover, TU was associated with rear-end collisions. Our experiments demonstrate that driver-initiated take-overs in automated driving do occur and that drivers' subsequent behavior may impair traffic safety. A better understanding of driver-initiated take-overs helps to increase the safety potential of automated vehicles, e.g., by designing assistance systems which will support drivers who initiate a take-over under critical, highly dynamic conditions.

How users of automated vehicles benefit from predictive ambient light displays

With the introduction of Level 3 and 4 automated driving, the engagement in a variety of non-driving related activities (NDRAs) will become legal. Previous research has shown that users desire information about the remaining time in automated driving mode and system status information to plan and terminate their activity engagement. In past studies, however, the positive effect of this additional information was realized when it was integrated in or displayed close by the NDRA. As future activities and corresponding items will be diverse, a device-independent and non-interruptive way of communication is required to continuously keep the user informed, thus avoiding negative effects on driver comfort and safety. With a set of two driving simulator studies, we have investigated the effectiveness of ambient light display (ALD) concepts communicating remaining time and system status when engaged in visually distracting NDRAs. In the first study with 21 participants, a traffic light color-coded ALD concept (LED stripe positioned at the bottom of the windshield) was compared to a baseline concept in two subsequent drives. Subjects were asked to rate usability, workload, trust, and their use of travel time after each drive. Furthermore, gaze data and NDRA disengagement timing was analyzed. The ALD with three discrete time steps led to improved usability ratings and lower workload levels compared to the baseline interface without any ALD. No significant effects on trust, attention ratio, travel time evaluation, and NDRA continuation were found, but a vast majority favored the ALD. Due to this positive evaluation, the traffic light ALD concept was subsequently improved and compared to an elapsing concept in a subsequent study with 32 participants. In addition to the first study, the focus was on the intuitiveness of the developed concepts. In a similar setting, results revealed no significant differences between the ALD concepts in subjective ratings (workload, usability, trust, travel time ratings), but advantages of the traffic light concept can be found in terms of its intuitiveness and the level of support experienced.

Does a faster takeover necessarily mean it is better? A study on the influence of urgency and takeover-request lead time on takeover performance and safety

During conditionally automated driving, drivers are sometimes required to take over control of the vehicle if a so-called takeover request (TOR) is issued. TORs are generally issued due to system limitations. This study investigated the effect of different urgency scenarios and takeover-request lead times (TORlts) on takeover performance and safety. The experiment was conducted in a real vehicle-based driving simulator. Manual driving, 7-second TORlt and 5-second TORlt were each tested. Participants experienced three progressively urgent driving scenarios: one cut-in scenario and two obstacle-avoidance scenarios. The results indicate that the TORlt significantly affected takeover performance and safety. Within a certain range, the longer the TORlt, the safer the takeover. However, while takeover reaction time depended mainly on the length of the TORlt and was not significantly related to other factors, such as workload, greater workloads that were caused by the TORlt were associated with shorter reaction times and decreased safety. This is evidence that the reaction time should not be used as the preferred indicator to evaluate takeover performance and safety. Indicators, such as workload, minimum TTC, feature point distribution position and slope of the obstacle avoidance trajectory, can better measure and evaluate takeover performance and safety. This study can provide data support for takeover safety evaluation of conditionally automated driving.

How drowsiness and distraction can interfere with take-over performance: A systematic and meta-analysis review

Drowsiness and distraction are major factors of road crashes and responsible of>35% of road fatalities. Automated driving could solve or minimize their impact, yet it is also in itself a way to promote them. Previous literature reviews and meta-analysis regarding take-overs during automated driving primarily focused on distraction rather than drowsiness. We thus present a systematic and meta-analysis literature review focused on the effect of distraction and drowsiness on take-over performance. From an initial selection of 1896 articles from databases, we obtained by applying systematic review methodology a total of 58 articles with 42 articles dedicated to distraction and 17 articles related to drowsiness. According to our analysis, we demonstrated that distraction and drowsiness increased the take-over request reaction time (TOR-RT), which could also lead to a reduction of the quality of take-overs. In addition, this longer reaction time was even more important in the case of handheld non-driving related tasks, which is important to consider as phone use is among the most frequent tasks done during automated driving. On a more methodological aspect, we also demonstrated that take-over time budget had a significant effect on TOR-RT. However, it is difficult to estimate to what extend distraction and drowsiness could impact the take-over quality, even if several elements supported safety issues. We underpinned several limits of the current methodologies applied in the study of distraction and drowsiness such as (i) the lack of additional measures related to the take-over quality (e.g., accelerations, collision rate), (ii) the many different methodologies applied to the determination of the TOR-RT (e.g., deactivation by the steering wheel, pedals, button), (iii) the high frequency of take-over requests which can lead to habituation effects, (iv) the lack of control conditions, (v) the fact that the level of drowsiness was relatively low in most studies. We thus highlighted recommendations for a better estimation of the effect of distraction and drowsiness on take-over performance. Further studies should adopt more standardized measures of TOR-RT and additional take-over quality measures, try minimizing the number of take-over requests, and carefully consider the time budget available for the use case since it influences the TOR-RT. Regarding distraction, researchers should consider the impact of tasks requiring handholding items. Concerning drowsiness, further protocols should consider the non-linearity of drowsiness and presence of micro sleeps and favor take-over requests based on drowsiness level protocols rather than on fixed duration protocols.

Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation

With the advent of autonomous driving, the issue of human intervention during safety-critical events is an urgent topic of research. Supervisory monitoring, taking over vehicle control during automation failures and then bringing the vehicle to safety under time pressure are cognitively demanding tasks that pose varying difficulties across the driving population. This underpins a need to investigate individual differences (i.e., how people differ in their dispositional traits) in driver responses to automation system limits, so that autonomous vehicle design can be tailored to meet the safety-critical needs of higher-risk drivers. However, few studies thus far have examined individual differences, with self-report measures showing limited ability to predict driver takeover performance. To address this gap, the present study explored the utility of an established brain activity-based objective index of trait attentional control (frontal theta/beta ratio; TBR) in predicting driver interactions with conditional automation. Frontal TBR predicted drivers' average takeover reaction time, as well as the likelihood of accident after takeover. Moving towards practical applications, this study also demonstrated the utility of streamlined estimates of frontal TBR measured from the forehead electrodes and from a single crown electrode, with the latter showing better fidelity and predictive value. Overall, TBR is behaviourally relevant, measurable with minimal sensors and easily computable, rendering it a promising candidate for practical and objective assessment of drivers' neurocognitive traits that contribute to their AV driving readiness.

The effect of inconsistent steering guidance during transitions from Highly Automated Driving

This driving simulator study investigated the effect of inconsistent steering guidance during system and user-initiated transitions from Highly Automated Driving (HAD). In particular, the aim of the study was to understand if steering conflicts could be achieved by adopting inconsistent steering guidance and whether these conflicts could be exploited to accelerate drivers' steering engagement within a limited time. Inconsistent steering guidance was generated by switching the guidance on and off at 3 different frequencies (0.1, 0.2 and 0.3 Hz). Results revealed that steering engagement has more to do with the initiation rather than the quality of the steering guidance. In fact, drivers were more engaged with the steering task when they initiated the transition themselves. Compared to system-initiated transitions, in user-initiated ones, drivers exerted stronger steering inputs throughout the transition, which allowed them to maintain larger Time To Lane Crossing (TTLC) values with fewer steering corrections. During system-initiated transitions, drivers started to actively engage with the steering activity only after more than 5 s from the start of the transition but were able to achieve a steering behaviour close to the one shown during user-initiated transitions at 10 s.

Takeover requests for automated driving: The effects of signal direction, lead time, and modality on takeover performance

Vehicle-to-driver takeover will still be needed in semi-autonomous vehicles. Due to the complexity of the takeover process, it is important to develop interfaces to support good takeover performance. Multimodal displays have been proposed as a candidate for the design of takeover requests (TORs), but many questions remain unanswered regarding the effectiveness of this approach. This study investigated the effects of takeover signal direction (ipsilateral vs. contralateral), lead time (4 vs. 7 s), and modality (uni-, bi-, and trimodal combinations of visual, auditory, and tactile signals) on automated vehicle takeover performance. Twenty-four participants rode in a simulated SAE Level 3 vehicle and performed a series of takeover tasks when presented with a TOR. Overall, single and multimodal signals with a tactile component were correlated with the faster takeover and information processing times, and were perceived as most useful. Ipsilateral signals showed a marginally significant benefit to takeover times compared to contralateral signals. Finally, a shorter lead time was associated with faster takeover times, but also poorer takeover quality. Findings from this study can inform the design of in-vehicle information and warning systems for next-generation transportation.

Automated vehicles that communicate implicitly: examining the use of lateral position within the lane

It may be necessary to introduce new modes of communication between automated vehicles (AVs) and pedestrians. This research proposes using the AV's lateral deviation within the lane to communicate if the AV will yield to the pedestrian. In an online experiment, animated video clips depicting an approaching AV were shown to participants. Each of 1104 participants viewed 28 videos twice in random order. The videos differed in deviation magnitude, deviation onset, turn indicator usage, and deviation-yielding mapping. Participants had to press and hold a key as long as they felt safe to cross, and report the perceived intuitiveness of the AV's behaviour after each trial. The results showed that the AV moving towards the pedestrian to indicate yielding and away to indicate continuing driving was more effective than the opposite combination. Furthermore, the turn indicator was regarded as intuitive for signalling that the AV will yield. Practitioner Summary: Future automated vehicles (AVs) may have to communicate with vulnerable road users. Many researchers have explored explicit communication via text messages and led strips on the outside of the AV. The present study examines the viability of implicit communication via the lateral movement of the AV.

Effects of different takeover request interfaces on takeover behavior and performance during conditionally automated driving

Automated driving is a developing trend that is coming to the consumer market, and conditionally automated driving (CAD) is anticipated to become the primary automated driving system. For enhancing both the comfort and security of human drivers in self-driving cars, the most significant concern of CAD is ensuring that not only can the driver conduct non-driving related tasks (NDRT) while automated driving is in progress, but also quickly and competently take over when the system reaches a limit and issues a takeover request (TOR). However, the level of distraction by NDRTs may affect the transition from automated driving to the human driver taking over. The focus of the present study was allowing a driver immersed in NDRTs to discover the TOR and take control of the driving quickly. A 3×2×2 factor experimental design was used: vehicle display interface information load (basic vs. prediction vs. advanced prediction interfaces); TOR information load (directional vs. non-directional information notifications); and degree of NDRT immersion (not performing vs. performing an NDRT when TOR prompt was issued). 48 participants were recruited, and different automotive display interfaces were used as TOR prompts with different information loads during driving to analyze the takeover behavior, performance, and subjective perception of the drivers, who were immersed in a smartphone-related task. The takeover process out of NDRT immersion was found to be more efficient with the advanced prediction interface, compared to the other two interfaces. All groups achieved faster takeovers and demonstrated better takeover performance if given directional rather than non-directional information, regardless of interface type or NDRT immersion.

Assessing subjective criticality of take-over situations: Validation of two rating scales

Assessing subjective criticality of take-over situations is crucial for understanding of take-over behavior and comparing studies. However, no validated rating scales exist that assess subjective criticality of take-over situations. In a driving simulator study, two rating scales, the Scale of Criticality Assessment of driving situations from Neukum et al. (2008) and the Criticality Rating Scale, were tested on their validity to assess the subjective criticality of take-over situations. Besides, the subjective and behavioral changes over the repeated experience of take-over situations were investigated. Twenty-five participants experienced a set of five take-over situations with varying time-to-collisions (TTC) at the moment of the take-over request, twice. After each of the first five take-over situations, participants rated the criticality on one scale, after each of the second five situations on the other scale. Correlation coefficients between TTCs and criticality ratings for each scale were calculated. Also, the changes of subjective and behavioral measures over the trials were investigated. Correlation coefficients indicated a strong correlation between criticality ratings and TTCs. Hence, both scales are equally valid for the assessment of the criticality of take-over situations. The repeated experience of the take-over situations did not affect effort ratings, take-over times, or steering wheel positions. But brake input decreased with increasing practice, indicating a safer take-over behavior. Hence, results of studies with repeated experience of take-over situations are relatively valid as only brake behavior changed with increasing practice.

Communication via motion - Suitability of automated vehicle movements to negotiate the right of way in road bottleneck scenarios

The introduction of automated vehicles (AVs) into urban areas initially leads to mixed traffic, consisting of AVs, human drivers, and vulnerable road users. Since the AV's passenger is no longer actively involved in the driving task, there may be changes in the interaction between AVs and surrounding human road users. Therefore, it is essential for an AV to behave in a comprehensible manner in order to maintain or even enhance traffic efficiency and traffic safety. This work investigates the interaction of an AV and a simultaneously oncoming human driver at road bottlenecks due to double-parked vehicles on both sides of the road. Based on findings derived from AV-pedestrian interaction, comfort limits in terms of driving dynamics, and traffic observations, we designed nine AV movements to either yield the right of way or to insist on it by varying the AV's speed (maintain speed, one-step deceleration, two-step deceleration) and its lateral offset (no offset, close offset, distant offset). The different vehicle movements were evaluated with 34 participants in a driving simulator study. The results show participants' shorter passing times, fewer crashes, and significantly higher ratings of the AV's communication if the AV movement contained a lateral offset. In addition to the regular encounters, we analyzed the controllability of an automation failure and its aftereffect on participants' trust in AVs. The experience of the automation failure reduced the trust rating significantly. From the results we conclude that the AV should communicate the right of way not only via speed adjustments but also via the performance of a lateral offset to enhance traffic efficiency and safety. Moreover, a change in the AV's maneuver due to an automation failure must be avoided since it is not controllable by the human driver.

What driving style makes pedestrians think a passing vehicle is driving automatically?

An important question in the development of automated vehicles (AVs) is which driving style AVs should adopt and how other road users perceive them. The current study aimed to determine which AV behaviours contribute to pedestrians' judgements as to whether the vehicle is driving manually or automatically as well as judgements of likeability. We tested five target trajectories of an AV in curves: playback manual driving, two stereotypical automated driving conditions (road centre tendency, lane centre tendency), and two stereotypical manual driving conditions, which slowed down for curves and cut curves. In addition, four braking patterns for approaching a zebra crossing were tested: manual braking, stereotypical automated driving (fixed deceleration), and two variations of stereotypical manual driving (sudden stop, crawling forward). The AV was observed by 24 participants standing on the curb of the road in groups. After each passing of the AV, participants rated whether the car was driven manually or automatically, and the degree to which they liked the AV's behaviour. Results showed that the playback manual trajectory was considered more manual than the other trajectory conditions. The stereotype automated 'road centre tendency' and 'lane centre tendency' trajectories received similar likeability ratings as the playback manual driving. An analysis of written comments showed that curve cutting was a reason to believe the car is driving manually, whereas driving at a constant speed or in the centre was associated with automated driving. The sudden stop was the least likeable way to decelerate, but there was no consensus on whether this behaviour was manual or automated. It is concluded that AVs do not have to drive like a human in order to be liked.

How should external human-machine interfaces behave? Examining the effects of colour, position, message, activation distance, vehicle yielding, and visual distraction among 1,434 participants

External human-machine interfaces (eHMIs) may be useful for communicating the intention of an automated vehicle (AV) to a pedestrian, but it is unclear which eHMI design is most effective. In a crowdsourced experiment, we examined the effects of (1) colour (red, green, cyan), (2) position (roof, bumper, windshield), (3) message (WALK, DON'T WALK, WILL STOP, WON'T STOP, light bar), (4) activation distance (35 or 50 m from the pedestrian), and (5) the presence of visual distraction in the environment, on pedestrians' perceived safety of crossing the road in front of yielding and non-yielding AVs. Participants (N = 1434) had to press a key when they felt safe to cross while watching a random 40 out of 276 videos of an approaching AV with eHMI. Results showed that (1) green and cyan eHMIs led to higher perceived safety of crossing than red eHMIs; no significant difference was found between green and cyan, (2) eHMIs on the bumper and roof were more effective than eHMIs on the windshield, (3) for yielding AVs, perceived safety was higher for WALK compared to WILL STOP, followed by the light bar; for non-yielding AVs, a red bar yielded similar results to red text, (4) for yielding AVs, a red bar caused lower perceived safety when activated early compared to late, whereas green/cyan WALK led to higher perceived safety when activated late compared to early, and (5) distraction had no significant effect. We conclude that people adopt an egocentric perspective, that the windshield is an ineffective position, that the often-recommended colour cyan may have to be avoided, and that eHMI activation distance has intricate effects related to onset saliency.

Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving

Automated driving systems are becoming increasingly prevalent throughout society. In conditionally automated vehicles, drivers may engage in non-driving-related tasks (NDRTs), which can negatively affect their situation awareness (SA) and preparedness to resume control of the vehicle, when necessary. Previous work has investigated engagement in NDRTs, but questions remain unanswered regarding its effect on drivers' SA during a takeover event. The objective of the current study is to use eye-tracking to aid in understanding how visual engagement in NDRTs affects changes in SA of the driving environment after a takeover request (TOR) has been issued. Thirty participants rode in a simulated SAE Level 3 automated driving environment and engaged in three separate pre-TOR tasks (Surrogate Reference Task, Monitoring Task, and Peripheral Detection Task) until presented with a TOR. Situation Awareness Global Assessment Technique (SAGAT) scores and gaze behavior were recorded during the post-TOR segment. Overall, longer times spent viewing the driving scene, and more dispersed visual attention allocation, were observed to be associated with better overall SA. Also, location-based eye tracking metrics show most promise in differentiating between task conditions with significantly different SAGAT scores. Findings from this work can inform the development of real-time SA assessment techniques using eye movements and ultimately contribute to improved operator roadway awareness for next-generation automated transportation.

Drivers' visual-distracted take-over performance model and its application on adaptive adjustment of time budget

There are certain situations that automated driving (AD) systems are still unable to handle, preventing the implementation of Level 5 AD. Thus, a transition of control, colloquially known as take-over of the vehicle, is required when the system sends a take-over request (TOR) upon exiting the operational design domain (ODD). An adaptive TOR along with good take-over performance requires adjusting the time budget (TB) to drivers' visual distraction state, adhering to a reliable visual-distraction-based take-over performance model. Based on a number of driving simulator experiments, the percentage of face orientation to distraction area (PFODA) and time to boundary at take-over timing (TTBT) were proposed to accurately evaluate the degree of visual distraction based on merely face orientation under naturalistic non-driving related tasks (NDRTs) and to evaluate take-over performance, respectively. In order to elucidate the safety boundary, this study also proposed an algorithm to set a suitable minimum value of the TTBT. Finally, a multiple regression model was built to describe the relationship among PFODA, TB and TTBT along with a corrected coefficient of determination of 0.748. Based on the model, this study proposed an adaptive TB adjustment method for the take-over system.

Cross-cultural differences in the acceptance of decisions of automated vehicles

Automated vehicles are expected to enhance driving safety and comfort. In order to fulfil these expectations, they have to be widely accepted and used. Implementing an acceptable driving style is therefore a must. Previous research on automated vehicle acceptance has largely concentrated on the effects of driving dynamics. This study takes a different approach and focuses on the effects of the driving decisions. To assess the effects of driving decisions on acceptance, an online experimental study was conducted in China, Germany, Japan and the US. Four overtaking scenarios, in which the automated vehicle took a decision, were presented as short texts. The situations differed with regard to the action (overtaking vs. stay in lane) and potential consequence (high or low hindrance of another driver). Participants then rated their acceptance. The results indicate that acceptance is dependent on the driving decisions and is further influenced by cultural background. Chinese drivers show high acceptance to the decisions and there were no significant differences between the presented scenarios. In the US and Germany, decisions leading to high hindrance of others are rejected, whereas in cases of low hindrance, overtaking is preferred. Japanese participants reject decisions, which lead to hindrance of others.

Sleep inertia in automated driving: Post-sleep take-over and driving performance

Sleep is emerging as a new driver state in automated driving. Post-sleep performance impairments due to sleep inertia, the transitional phase from sleep to wakefulness that can take up to 30 min, are a potential safety issue. Take-over performance immediately after sleep is impaired and drivers perceive the take-over as critical. The aim of the presented study was to assess take-over behavior immediately after sleep and driving behavior during the 10 min after sleep. A study with N = 31 drivers was conducted in a high-fidelity driving simulator. Take-over performance and driving performance were assessed a) under alert baseline conditions and b) after awakening from electroencephalography-confirmed stable sleep. Take-over performance 15 s after awakening was impaired resulting in more driving errors compared to the alert baseline. Lane keeping was dramatically impaired in the first 3 min after sleep and recovered rapidly. Drivers drove slower after sleep and speed keeping was less stable for at least 10 min. The results suggest that human-machine interaction design should account for the drivers' impaired post-sleep driving performance.

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4 s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7 s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.

Effects of explaining system failures during maneuver coordination while driving manual or automated

To support the coordination of road users in situations like merging or turning left, an advanced driver assistance system for cooperative driving could be helpful whether driving manually or automated. This simulator study investigated the behavior of drivers being confronted with system failures. In two test situations with system failures (loss of communication of the system and change of traffic environment), the system could not complete the coordination properly and the driver was informed about the system failure and the abortion of maneuver coordination. The focus of this study was to analyze the effect of system failures on drivers' trust in the system and whether an explanatory message provided by the system would increase acceptance. Therefore, subjective data as well as gaze and physiological data of 32 participants were analyzed. The results revealed decreased trust in the system after experiencing a system failure, but no long term effect was found. The drivers evaluated the timing, as well as the content, of the explanatory message as appropriate. The explanations were perceived as helpful, but no effect on acceptance was found.

A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles

A hierarchical hybrid control system is proposed to cope with highly automated driving in highway environments with multiple lanes and surrounding vehicles. In the high-level layer, the discrete driving decisions are coordinated by the finite-state machine (FSM) based on the relative position identification and predictive longitudinal distance of the surrounding vehicles. The low-level layer is responsible for the vehicle motion control, where the model predictive control (MPC) approach is utilized to integrate the longitudinal and lateral control mainly including car-following control and lane changing control. The proposed control system focuses on two issues regarding safe driving on highways. On one hand, the subject vehicle must always keep a safe distance with its leading vehicle to avoid the rear-end collision. On the other hand, the subject vehicle should also overtake the preceding vehicle by safe lane changes if the desired speed is not achieved. The effectiveness of the hybrid control is tested in the simulation, whose results verify that the driving decisions are made reasonably and the vehicle motion control obeys stability and comfort requirements. Moreover, it is also indicated by the simulations in random scenarios that the control strategy is able to deal with most of ordinary situations on highways although some emergency situations or critical driving maneuvers of other vehicles are not considered.

Expert Drivers' Prospective Thinking-Aloud to Enhance Automated Driving Technologies - Investigating Uncertainty and Anticipation in Traffic

Current automated driving technology cannot cope in numerous conditions that are basic daily driving situations for human drivers. Previous studies show that profound understanding of human drivers' capability to interpret and anticipate traffic situations is required in order to provide similar capacities for automated driving technologies. There is currently not enough a priori understanding of these anticipatory capacities for safe driving applicable to any given driving situation. To enable the development of safer, more economical, and more comfortable automated driving experience, expert drivers' anticipations and related uncertainties were studied on public roads. First, driving instructors' expertise in anticipating traffic situations was validated with a hazard prediction test. Then, selected driving instructors drove in real traffic while thinking aloud anticipations of unfolding events. The results indicate sources of uncertainty and related adaptive and social behaviors in specific traffic situations and environments. In addition, the applicability of these anticipatory capabilities to current automated driving technology is discussed. The presented method and results can be utilized to enhance automated driving technologies by indicating their potential limitations and may enable improved situation awareness for automated vehicles. Furthermore, the produced data can be utilized for recognizing such upcoming situations, in which the human should take over the vehicle, to enable timely take-over requests.

Comparing dynamic and static illustration of an HMI for cooperative driving

The study analyses the Human-Machine-Interface (HMI) of a driver assistance system for cooperative driving, such as merging or turning left situations. Three versions of the HMI are varied as independent variables within subjects. Two versions, displayed in the instrument cluster, focus either on a dynamic or a static illustration of the current status of the system. The third HMI, developed in a preliminary study, serves as benchmark to compare the cluster-based HMIs. The benchmark HMI uses the same status messages and highlights the partner directly in the environment by augmented reality elements. The results of the present study show that the Benchmark best supported cooperative behavior. Both versions of the HMI located in the instrument cluster also support cooperative behavior and are accepted by the drivers. However, more glances are shifted from the relevant area in the driving scenario towards the cluster compared to the Benchmark HMI. With the static version, the participants felt more distracted compared to the dynamic HMI. In conclusion, as long as it is not technically possible to display the partner directly in the environment, a dynamic display in cooperation situations is a good alternative.

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.

Understanding take-over performance of high crash risk drivers during conditionally automated driving

Understanding driver behavior of conditionally automated driving is necessary to ensure a safe transition from automated to manual driving. This study aimed to examine the difference in take-over performance between high crash risk (HCR) and lower crash risk (LCR) drivers in emergency take-over situations during conditionally automated driving. In the current simulator study, a 3 × 3 (within-subjects) factorial design was used, including the task factors (no task, reading the news, and watching a video) and time budget factors (time budget = 3 s, 4 s, and 5 s). Forty-eight participants completed a test drive on an approximately 10 km long two-way six-lane urban road. The participants firstly were in manual control and then switched to the automated driving mode at a speed of 50 km/h. The automated driving system was able to detect a broken car in the ego-lane and requested the driver to take over the control of the vehicle. There are at least one or two other vehicles or motorcycles on each side of the ego-vehicle, resulting in fewer escape paths. For the two non-handheld non-driving-related tasks (NDRTs), the participants were asked to be fully engaged in a task without any need to monitor the road environments. Each participant completed nine emergency take-over situations. The participants were classified into two groups that were labeled LCR (N ≤ 2) and HCR drivers (N ≥ 3) according to the number of accidents per driver. The results show that LCR drivers had shorter brake reaction time compared to HCR drivers. For all drivers, the engagement in a task led to longer response times, and the time budget affected the longitudinal vehicle control. In addition, the task affected the response times for LCR and HCR drivers, but only the time budget affected the longitudinal vehicle control for LCR drivers. For all drivers, LCR and HCR drivers, the time budget and task affected the safety of take-over. Especially, the two non-handheld everyday tasks seem to have a similar effect on the drivers' workload. Therefore, the HCR drivers had a lower hazard perception compared to the LCR drivers, and the factor regarding the individual difference of driving ability in take-over situations should be considered to design safe take-over concepts for automated vehicles.

Automated driving: A biomechanical approach for sleeping positions

Occupants of autonomous vehicle have frequently indicated the desire to sleep or rest while driving, yet little has been known regarding the suitable design criteria for a biomechanically reasoned in-vehicle sleeping position. This study was aimed at evaluating the biomechanical quality of different backrest and seat pan angle combinations, and at predicting the most favourable sleeping positions based on vehicle restriction. More specifically, the interface pressure distribution and subjective suitability rating of 23 subjects was assessed in a total of nine (3 × 3) combinations of seat pan (20°, 30°, 40°) and backrest (145°, 155°, 165°) angles. Biomechanical quality was evaluated with an interface pressure score (IPS) based on sensitivity weighted pressures and the total contact area. Two-way repeated measures ANOVA revealed that IPS significantly improves with increasing seat pan angle whereas backrest angles of 155° or 165° lead to significant better IPS compared to flatter ones (145°). The overall highest IPS was observed for a 40°-seat pan angle in combination with a 155°-backrest angle. Subjective suitability rating revealed that people prefer a combination of 165° backrest angle with a seat pan of 20°; however, eight of nine combinations can be considered as suitable for sleeping. Therefore, the combination of a 40°-seat pan angle and 155° backrest is recommended by the present study for an in-vehicle sleeping position due to the increased biomechanical quality.

Effects of cognitive and visual loads on driving performance after take-over request (TOR) in automated driving

The present study investigated effects of cognitive and visual loads on driving performance after take-over request (TOR) in an automated driving task. Participants completed automated driving in a driving simulator without a non-driving related task, with an easy non-driving related task, and with a difficult non-driving related task. The primary task was to monitor the environment and the system state. An N-back task and a Surrogate Reference Task (SuRT) were adapted to induce cognitive and visual loads respectively. The system followed a front vehicle automatically. Driving performance was measured by responses to a critical event (appearance of a broken-down car) after the automated system issued TOR and then terminated. High subjective difficulty of the N-back task was related to increased time and increased steering angle variance in the time course from onset of steering control to lane change, while high subjective difficulty of SuRT was related to increased steering angle variance in the time course after lane change. This suggests that both cognitive and visual loads affect driving performance after TOR in automated driving, but the effects appear in different time courses.

Optimizing the safety-efficiency balancing of automated vehicle car-following

This paper proposes an approach to rationally set automated vehicles' car following behavior that explicitly balances between the competing considerations of safety (i.e. small probabilities of a high-consequence crash) and efficiency (guaranteed but small impacts on journey arrival time due to the choice of car following distance). The specification of safety and efficiency are both based on empirically supported concepts and data. In numerical analyses with empirical vehicle trajectories at two sites, we demonstrate intuitive response to systematic variation in numerical values selected as inputs, as well as whether the scope of the efficiency consideration is selfish or systemwide. The proposed balancing is aligned with the standard "Hand Rule" criterion to demonstrate that a duty of care has been met, in which a burden must be borne if it is less than the product of the probability of loss to a third party and the magnitude of loss. Thus the proposed approach is intended to be useful for designers of control algorithms for AVs to establish that they have met their duty of care, taking both safety and efficiency into account.

Age-related differences in effects of non-driving related tasks on takeover performance in automated driving

INTRODUCTION

During SAE level 3 automated driving, the driver's role changes from active driver to fallback-ready driver. Drowsiness is one of the factors that may degrade driver's takeover performance. This study aimed to investigate effects of non-driving related tasks (NDRTs) to counter driver's drowsiness with a Level 3 system activated and to improve successive takeover performance in a critical situation. A special focus was placed on age-related differences in the effects.

METHOD

Participants of three age groups (younger, middle-aged, older) drove the Level 3 system implemented in a high-fidelity motion-based driving simulator for about 30 min under three experiment conditions: without NDRT, while watching a video clip, and while switching between watching a video clip and playing a game. The Karolinska Sleepiness Scale and eyeblink duration measured driver drowsiness. At the end of the drive, the drivers had to take over control of the vehicle and manually change the lane to avoid a collision. Reaction time and steering angle variability were measured to evaluate the two aspects of driving performance.

RESULTS

For younger drivers, both single and multiple NDRT engagements countered the development of driver drowsiness during automated driving, and their takeover performance was equivalent to or better than their performance without NDRT engagement. For older drivers, NDRT engagement did not affect the development of drowsiness but degraded takeover performance especially under the multiple NDRT engagement condition. The results for middle-aged drivers fell at an intermediate level between those for younger and older drivers. Practical Applications: The present findings do not support general recommendations of NDRT engagement to counter drowsiness during automated driving. This study is especially relevant to the automotive industry's search for options that will ensure the safest interfaces between human drivers and automation systems.

The most difficult at-fault fatal crashes to avoid with current active safety technology

OBJECTIVE

We studied which current fatal at-fault crashes would occur despite the most advanced current active safety devices (up to SAE level 2 of driving automation) and how frequent these crashes would be.

METHODS

We carried out a cross-sectional study of passenger cars that were first registered during the period 1st January 2010 to 31st December 2017 in Finland. To gain the true exposure for these cars, we accessed the national Vehicular and Driver Data Register to obtain the mileage information and the registration count for the study period of 2010-17. Similarly, we accessed the registry of Finnish road accident investigation teams and included all fatal at-fault crashes among the cars in our study for the same period. We used a real world reference technology for each active safety system in our analysis and chose one car brand as an example. This gave us exact system specifications and enabled testing the operation of the systems on the road. We performed field tests to gain further information on the precise operation of the safety systems in different operating conditions. Finally, we gathered all information on the studied active safety systems and analyzed the investigated at-fault fatal crashes case-by-case using our four level method.

RESULTS

Cars in our study were the primary party in 113 investigated fatal accidents during the years 2010-17. In 87 of the accidents, the leading cause of death was the injuries due to the crash, and these cases were classified as "unavoidable" (n = 58, 67 %), "avoidable" (n = 26, 30 %) or unsolved (n = 3, 3 %). Of the 58 "unavoidable" crashes 21 (36 %) were suicides, 21 (36%) involved active driver input which would have prevented the safety system operation, 15 (17 %) featured circumstances beyond the safety system performance and in one loss-of-control crash the driver had disabled the relevant safety system (electronic stability control). The registration years of the cars in our study (2010-17) totaled 3,772,864 and during this period, the cars travelled 75.9 billion kilometers. The crash incidence of the "unavoidable" at-fault fatal crashes was 0.76-0.80 fatal crashes per billion kilometers and 15-16 fatal crashes per million registration years.

CONCLUSIONS

We calculated a crash incidence for the "unavoidable" crashes which was 20-27% smaller than the observed crash rate of ESC-fitted passenger cars in our previous study. We concluded that suicides, active driver input until the crash, and challenging weather and road conditions are the most difficult factors for current active safety systems. Our analysis did not account for issues such as system usability or driver acceptance and therefore our results should be regarded as something that is currently theoretically achievable. However, the observed incidence is a good reference for automated driving development and the crash rate of automated cars.

Exploring causes and effects of automated vehicle disengagement using statistical modeling and classification tree based on field test data

Automated vehicles (AV) testing on the public roads is ongoing in several states in the US as well as in Europe and Asia. As long as the automated vehicle technology has not achieved full automation (Level 5), human drivers are still expected to take over the steering wheel and throttles when there is an automated vehicle disengagement. However, contributing factors and the mechanism about automated vehicle-initiated disengagement has not been quantitatively and comprehensively explored and investigated due to the lack of field test data. Besides, understanding human drivers' perception and promptness of reaction to the AV disengagement is essential to ensure safety transition between automated and manual driving. By harnessing California's Autonomous Vehicle Disengagement Report Database, which includes the AV disengagement data from field tests in 2016-2017, this paper quantitatively investigated the AV disengagement using multiple statistical modeling approaches that involve statistical modeling and classification tree. Specifically, the paper identifies the contributing factors impacting human drivers' promptness to AV disengagements, and quantitatively investigates the underlying causes to AV disengagements. Results indicate that current AV disengagement on public roads is dominated by causes due to a planning issue. The cause of an AV disengagement is significantly induced by lacking certain numbers of radar and LiDAR sensors installed on the automated vehicles. These thresholds of these sensors needed are revealed. Cause of disengagement and roadway characteristics significantly impact drivers' take-over time when facing an AV disengagement. AV perception or control issue-based disengagement can significantly extend drivers' perception-reaction time to take over the driving. The quantitative knowledge obtained ultimately facilitates revealing the mechanisms of the automated vehicle disengagements to ensure safe AV operations on public roads.

Asleep at the automated wheel-Sleepiness and fatigue during highly automated driving

Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schömig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with Nö=ö60 drivers we used a three factorial 2ö×ö2ö×ö12 mixed design to analyze the progression (12ö×ö5ömin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers' reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving. Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35ömin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40ömin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mö=ö3.2; SDö=ö2.1ös) compared to the reaction times after a long drive (Mö=ö2.4; SDö=ö0.9ös). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it. Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could pose a serious hazard in complex take-over situations where situation awareness is required to prepare for threats. Driver fatigue monitoring or controllable distraction through non-driving tasks could be necessary to ensure alertness and availability during highly automated driving.

Effects of scheduled manual driving on drowsiness and response to take over request: A simulator study towards understanding drivers in automated driving

Because current automated vehicles have operational limitations, it is important to ensure that the fallback-ready driver is able to perform appropriately when required to take over control of the vehicle. However, time-related increase in driver drowsiness is well-known, and drowsy driving can affect response to take-over request (TOR). It was previously reported that a scheduled period of manual driving during automated driving was beneficial in maintaining driver arousal level. The present driving simulator study investigates the effects of scheduled manual driving on driver drowsiness and performance, as well as age differences therein. A total of 115 participants, whose gender was balanced and age was distributed uniformly from 20 to 70 years, drove an automated vehicle for 31 min, and a TOR was prompted before a collision event. A between-subjects design comprised two conditions: with versus without a scheduled 10-min interval of manual driving that ended 10 min before TOR. The Karolinska Sleepiness Scale and eyeblink durations estimated from electrooculograms (EOG) were used to subjectively and objectively measure participant's drowsiness. Reaction time, standard deviation of steering wheel angle, and minimum Time-to-Collison (TTC) were extracted to measure driver performance in response to TOR. The alleviating effect on drowsiness of 10-min scheduled manual driving became non-significant after another 10-min period of automated driving. Although the scheduled manual driving had no significant effect for younger drivers, older drivers reacted significantly more slowly in both steering and braking at the critical event. These findings provide essential insights for human-vehicle interactions: Scheduled manual driving cannot maintain drivers' arousal level for 10 min afterwards, and for older drivers, it would be better to avoid unnecessary task-switching between manual and automated driving.

Test procedure for evaluating the human-machine interface of vehicles with automated driving systems

Objective: The human-machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that-depending on the operational design domain (ODD)-different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator's responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this. Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA's automated vehicle policy are met. Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics. Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.

A framework for definition of logical scenarios for safety assurance of automated driving

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.

Improvement of driver active interventions during automated driving by displaying trajectory pointers-A driving simulator study

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human-machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle. Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed. The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary. The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10 min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object. Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05). Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.

Functional decomposition-A contribution to overcome the parameter space explosion during validation of highly automated driving

Objective: Particular testing by functional decomposition of the automated driving function can potentially contribute to reducing the effort of validating highly automated driving functions. In this study, the required size of test suites for scenario-based testing and the potential to reduce it by functional decomposition are quantified for the first time. Methods: The required size of test suites for scenario-based approval of a so-called Autobahn-Chauffeur (SAE Level 3) is analyzed for an exemplary set of scenarios. Based on studies of data from failure analyses in other domains, the possible range for the required test coverage is narrowed down and suitable discretization steps, as well as ranges for the influence parameters, are assumed. Based on those assumptions, the size of the test suites for testing the complete system is quantified. The effects that lead to a reduction in the parameter space for particular testing of the decomposed driving function are analyzed and the potential to reduce the validation effort is estimated by comparing the resulting test suite sizes for both methods. Results: The combination of all effects leads to a reduction in the test suites' size by a factor between 20 and 130, depending on the required test coverage. This means that the size of the required test suite can be reduced by 95-99% by particular testing compared to scenario-based testing of the complete system. Conclusions: The reduction potential is a valuable contribution to overcome the parameter space explosion during the validation of highly automated driving. However, this study is based on assumptions and only a small set of exemplary scenarios. Thus, the findings have to be validated in further studies.