Secondary task engagement and disengagement in the context of highly automated driving

During highly automated driving (level 3 automation according to SAE International, 2014) people are likely to increase the frequency of secondary task interactions. However, the driver must still be able to take over control within a reasonable amount of time. Previous studies mainly investigated take-over behavior by forcing participants to engage in secondary tasks prior to take over, and barely addressed how drivers voluntarily schedule secondary task processing according to the availability and predictability of automated driving modes. In the current simulator study 20 participants completed a test drive with alternating sections of manual and highly automated driving. One group had a preview on the availability of the automated driving system in upcoming sections of the track (predictive HMI), while the other drivers served as a control group. A texting task was offered during both driving modes and also prior to take-over situations. Participants were free to accept or reject a given task, taking the situational demands into account. Drivers accepted more tasks during highly automated driving. Furthermore, tasks were rejected more often prior to take-over situations in the predictive HMI group. This was accompanied by safer take-over performance. However, once engaged in a task, drivers tended to continue texting even in take-over situations. The results indicate the need to discriminate different aspects of task handling regarding self-regulation: task engagement and disengagement.     

基于认知机制的有条件自动驾驶接管中的人因问题

自动驾驶能在很大程度上缓解现代交通问题并提升驾驶舒适度。有条件自动驾驶下, 驾驶员可执行非驾驶相关任务但需要在系统无法处理的状况下接管车辆。在这一关键过程中, 驾驶员需要进行注意转换并获得情境意识以成功接管。已有研究表明, 接管请求、非驾驶相关任务、驾驶情景及驾驶员因素是影响接管过程重要因素。未来可从认知机制角度研究各因素对接管过程产生的影响, 以及探究接管过程中各因素之间可能存在的交互作用。     

Investigating explanations in conditional and highly automated driving: The effects of situation awareness and modality

With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially in unexpected driving scenarios. Although it might be not necessary to require the drivers to intervene on most occasions, it is still important to improve drivers’ situation awareness (SA) in unexpected driving scenarios to improve their trust in and acceptance of AVs. In this study, we conceptualized SA at the levels of perception (SA L1), comprehension (SA L2), and projection (SA L3), and proposed an SA level-based explanation framework based on explainable AI. Then, we examined the effects of these explanations and their modalities on drivers’ situational trust, cognitive workload, as well as explanation satisfaction. A three (SA levels: SA L1, SA L2 and SA L3) by two (explanation modalities: visual, visual + audio) between-subjects experiment was conducted with 340 participants recruited from Amazon Mechanical Turk. The results indicated that by designing the explanations using the proposed SA-based framework, participants could redirect their attention to the important objects in the traffic and understand their meaning for the AV system. This improved their SA and filled the gap of understanding the correspondence of AV’s behavior in the particular situations which also increased their situational trust in AV. The results showed that participants reported the highest trust with SA L2 explanations, although the mental workload was assessed higher in this level. The results also provided insights into the relationship between the amount of information in explanations and modalities, showing that participants were more satisfied with visual-only explanations in the SA L1 and SA L2 conditions and were more satisfied with visual and auditory explanations in the SA L3 condition. Finally, we found that the cognitive workload was also higher in SA L2, possibly because the participants were actively interpreting the results, consistent with a higher level of situational trust. These findings demonstrated that properly designed explanations, based on our proposed SA-based framework, had significant implications for explaining AV behavior in conditional and highly automated driving.     

Take-over requests in highly automated driving: A crowdsourcing survey on auditory,vibrotactile, and visual displays

An important research question in the domain of highly automated driving is how to aid drivers in transitions between manual and automated control. Until highly automated cars are available, knowledge on this topic has to be obtained via simulators and self-report questionnaires. Using crowdsourcing, we surveyed 1692 people on auditory, visual, and vibrotactile take-over requests (TORs) in highly automated driving. The survey presented recordings of auditory messages and illustrations of visual and vibrational messages in traffic scenarios of various urgency levels. Multimodal TORs were the most preferred option in high-urgency scenarios. Auditory TORs were the most preferred option in low-urgency scenarios and as a confirmation message that the system is ready to switch from manual to automated mode. For low-urgency scenarios, visual-only TORs were more preferred than vibration-only TORs. Beeps with shorter interpulse intervals were perceived as more urgent, with Stevens’ power law yielding an accurate fit to the data. Spoken messages were more accepted than abstract sounds, and the female voice was more preferred than the male voice. Preferences and perceived urgency ratings were similar in middle- and high-income countries. In summary, this international survey showed that people’s preferences for TOR types in highly automated driving depend on the urgency of the situation.     

The influence of system transparency on trust: Evaluating interfaces in a highly automated vehicle

Previous studies indicate that, if an automated vehicle communicates its system status and intended behaviour, it could increase user trust and acceptance. However, it is still unclear what types of interfaces will better portray this type of information. The present study evaluated different configurations of screens comparing how they communicated the possible hazards in the environment (e.g. vulnerable road users), and vehicle behaviours (e.g. intended trajectory). These interfaces were presented in a fully automated vehicle tested by 25 participants in an indoor arena. Surveys and interviews measured trust, usability and experience after users were driven by an automated low-speed pod. Participants experienced four types of interfaces, from a simple journey tracker to a windscreen-wide augmented reality (AR) interface which overlays hazards highlighted in the environment and the trajectory of the vehicle. A combination of the survey and interview data showed a clear preference for the AR windscreen and an animated representation of the environment. The trust in the vehicle featuring these interfaces was significantly higher than pretrial measurements. However, some users questioned if they want to see this information all the time. One additional result was that some users felt motion sick when presented with the more engaging content. This paper provides recommendations for the design of interfaces with the potential to improve trust and user experience within highly automated vehicles.     

Semi-automated versus highly automated driving in critical situations caused by automation failures

The purpose of this study was to examine the effects of vehicle automation and automation failures on driving performance. Previous studies have revealed problems with driving performance in situations with automation failures and attributed this to drivers being out-of-the-loop. It was therefore hypothesized that driving performance is safer with lower than with higher levels of automation. Furthermore, it was hypothesized that driving performance would be affected by the extent of the automation failure. A moving base driving simulator was used. The design contained semi-automated and highly automated driving combined with complete, severe, and moderate deceleration failures. In total the study involved 36 participants. The results indicate that driving performance degrades when the level of automation increases. Furthermore, it is indicated that car drivers are worse at handling complete than partial deceleration failures.     

Active vehicle pitch motion for communication in automated driving

The number of automated vehicles (AVs) is expected to successively increase in the near future. This development has a considerable impact on the informal communication between AVs and pedestrians. Informal communication with the driver will become obsolete during the interaction with AVs. Literature suggests that external human machine interfaces (eHMIs) might substitute the communication between drivers and pedestrians. In the study, we additionally test a recently discussed type of communication in terms of artificial vehicle motion, namely active pitch motion, as an informal communication cue for AVs.N = 54 participants approached AVs in a virtual inner-city traffic environment. We explored the effect of three communication concepts: an artificial vehicle motion, namely active pitch motion, eHMI and the combination of both. Moreover, vehicle types (sports car, limousine, SUV) were varied. A mixed-method approach was applied to investigate the participantś crossing behavior and subjective safety feeling. Furthermore, eye movement parameters were recorded as indicators for mental workload.The results revealed that any communication concept drove beneficial effects on the crossing behavior. The participants crossed the road earlier when an active pitch motion was present, as this was interpreted as a stronger braking. Further, the eHMI and a combination of eHMI and active pitch motion had a positive effect on the crossing behavior. The active pitch motion showed no effect on the subjective safety feeling, while eHMI and the combination enhanced the pedestrianś safety feeling while crossing. The use of communication resulted in less mental workload, as evidenced by eye-tracking parameters. Variations of vehicle types did not result in significant main effects but revealed interactions between parameters. The active pitch motion revealed no learning. In contrast, it took participants several trials for the eHMI and the combination condition to affect their crossing behavior. To sum up, this study indicates that communication between AVs and pedestrians can benefit from the consideration of vehicle motion.     

Takeover performance of older drivers in automated driving: A review

IntroductionThe introduction of automated vehicles to the road environment brings new challenges for older drivers. Level 3 of conditional automation requires drivers to take over control of their vehicle whenever the automated system reaches its limits. Even though autonomous vehicles may be of great benefit to older drivers in terms of safely maintaining their mobility, a better understanding of their takeover performance remains crucial. The objective of this review of the literature is to shed more light on the effects that aging has on takeover performance during automated driving.MethodsThree database searches were conducted: PsychINFO, Web Of Sciences, and TRID. Studies from the last decade which included groups of older drivers were reviewed.ResultsAfter checking through abstracts and texts of articles, 9 articles, 4 proceedings papers, and 1 technical report were included in this review. All studies included a driving simulator that refers to level 3 of automation (which requires supervision by the driver). Five out of fourteen studies showed that older adults had poorer takeover performance (in terms of takeover time and takeover quality) than younger adults. However, several factors, such as the type of non-driving related task (NDRT), were seen to influence takeover performance in older adults. Speed, type and duration of notification interval, distribution and duration of driving modes, and number of takeovers were all also factors of influence.ConclusionThis review synthesizes the results of 14 articles which investigate the effects of age-related changes on takeover performance. Various external factors as NDRTs, speed, type and duration of notification to take over, duration of the automated phase, distribution of the automated/manual phases may affect takeover performance in older adults. Even if the majority of articles showed that older adults are globally slower at taking over a vehicle than younger adults, findings concerning take over quality yield divergent results. It's probably due to age related cognitive changes, particularly in executive functions or to a great heterogeneity in this population. This literature review highlights the need to develop new research on the impact of aging on takeover performance.     

A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

Human factors of transitions in automated driving: A general framework and literature survey

The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control, longitudinal control, and monitoring) between the driver and the automation. A transition in automated driving is defined as the process during which the human-automation system changes from one driving state to another, with transitions of monitoring activity and transitions of control being among the possibilities. Based on ‘Is the transition required?’, ‘Who initiates the transition?’, and ‘Who is in control after the transition?’, we define six types of control transitions between the driver and automation: (1) Optional Driver-Initiated Driver-in-Control, (2) Mandatory Driver-Initiated Driver-in-Control, (3) Optional Driver-Initiated Automation-in-Control, (4) Mandatory Driver-Initiated Automation-in-Control, (5) Automation-Initiated Driver-in-Control, and (6) Automation-Initiated Automation-in-Control. Use cases per transition type are introduced. Finally, we interpret previous experimental studies on transitions using our framework and identify areas for future research. We conclude that our framework of driving states and transitions is an important complement to the levels of automation proposed by transportation agencies, because it describes what the driver and automation are doing, rather than should be doing, at a moment of time.     

Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes

Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.     

Effects of visual factors during automated driving of mobility scooters on user comfort: An exploratory simulator study

An automated mobility scooter is expected to provide convenient and safe transportation for users in their living area. However, there is limited research on user comfort compared to that on user safety for the automated driving of mobility scooters. Because the user does not perform driving tasks in automated driving, the visual information from the peripheral environment and visual behavior is expected to closely affect the psychological comfort of the user. This study clarifies the effects of factors related to the automated driving of mobility scooters and the peripheral environment on the visual behavior and psychological comfort of the user. Effects of driving velocity and pedestrian density on the visual behavior and psychophysiological responses of users were investigated via a driving simulator. The results showed that automated driving in an environment with a high pedestrian density can result in a decrease in fixation duration, deactivation of visual processing, sympathetic activation, and feeling of negative emotion. This implies that the assessment of visual behaviors of users is important for the design of automated mobility scooters to improve user comfort.     

Effects of secondary tasks and display position on glance behavior during partially automated driving

The driving task is becoming increasingly automated, thus changing the driver’s role. Moreover, in-vehicle information systems using different display positions and information processing channels might encourage secondary task engagement. During manual driving scenarios, varying secondary tasks and display positions could influence driver’s glance behavior. However, their impact on the driver’s capability to monitor the partially automated driving system has not yet been determined. The current study assessed both the effects of different secondary tasks (Surrogate Reference Task (SuRT) vs. text reading) and display positions (head-up display (HUD) vs. center console) on driver’s glance behavior during partially automated driving in a simulated car following task. Different automation system failures regarding the lateral and longitudinal control occurred while driving. Furthermore, participants’ reported advantages, disadvantages and preferences regarding the investigated display positions as well as regarding the secondary task engagement during partially automated driving in general. Mixed design ANOVAs revealed that the HUD yielded considerably longer eyes-on display time (total and mean glance durations) than the center console. Moreover, the text reading task resulted in longer total and mean glance durations than the SuRT. Similar to manual driving scenarios, the results showed a consistent effect of display position and secondary task on the driver’s glance behavior. Despite the longer eyes-on display time for the HUD, its proximity to the driving environment might enable a faster identification of and reaction to critical situations (e.g., due to system failures). Participants would prefer the HUD as display position compared to the center console. Regarding secondary task engagement during partially automated driving participants seemed to be aware of the benefits but also of the risks.     

Adaptation of the safe driving climate among friends scale in Chinese drivers and its associations with risky driving behaviours

The present study aimed to adapt the Safe Driving Climate among Friends Scale (SDCaF) to Chinese drivers and to examine its reliability and validity. Three hundred and sixty drivers aged from 18 to 24 years old were asked to complete the SDCaF and the Risky Driving Behaviour Scale. A confirmatory factor analysis (n = 360) was conducted to examine the factorial structure of the SDCaF. The validity of the scale was then evaluated by examining the associations between the SDCaF factors, risky driving behaviours and traffic violations. The CFA results showed that the model fit of the Chinese version of the scale (SDCaF-C) was acceptable. Second, the SDCaF-C factors were weakly or moderately correlated with speeding, self-assertiveness and rule violations. Third, significant gender differences were found for the variables of friend pressure and communication, with male drivers scoring higher than female drivers. Moreover, drivers who had traffic violations in the past year scored higher on friend pressure and lower on both communication and shared commitment to safe driving compared to those who had not had traffic violations. The findings supported the psychological properties of the SDCaF-C and highlighted the importance of concerning the effects of safe diving climate among friends on young drivers’ risky driving behaviours.     

Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.     

The potential of gamification for user education in partial and conditional driving automation: A driving simulator study

Drivers must establish adequate mental models to ensure safe driver-vehicle interaction in combined partial and conditional driving automation. To achieve this, user education is considered crucial. Since gamification has previously shown positive effects on learning motivation and performance, it could serve as a measure to enhance user education on automated vehicles. We developed a tablet-based instruction involving gamified elements and compared it to instruction without gamification and a control group receiving a user manual. After instruction, participants (N = 57) experienced a 30-minute automated drive on a motorway in a fixed-base driving simulator. Participants who received the gamified instruction reported a higher level of intrinsic motivation to learn the provided content. The results also indicate that gamification promotes mental model formation and trust during the automated drive. Taken together, including gamification in user education for automated driving is a promising approach to enhance safe driver-vehicle interaction.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving

Automated driving comes with many promises like zero traffic casualties that are, however, only realizable given their technological development and public acceptance for wide-spread deployment. To investigate the potential acceptance, we developed a new data-driven questionnaire focusing on drivers and barriers of the anticipated possible (non-)adoption of automated driving (AD). Therefore, we conducted a cross-sectional questionnaire study with 725 respondents (351 female, 374 male) ranging from 18 to 96 years. We applied exploratory and confirmatory factor analyses and structural equation modeling, to pursue the overarching goal to develop the QAAD questionnaire (short and long version for SAE Level 3 (L3) and 5 (L5) AD). Hence, we identified the three latent factors PRO (positive aspects), CON (negative aspects), and NDRTs (non-driving related tasks) of L3 (short: 9 items; long: 16) and L5 (short: 11, long: 17), respectively. Additionally, we queried general questions on AD (8 items) and extracted the two factors Early Adoption/Pro AD and Sustainability. Our findings and the goodness-of-fit indices suggest data-driven models for L3 and L5 automated driving and on general aspects focusing on early adoption and sustainability in the context of AD. They can be applied in future research settings, in particular, in (quasi-)experimental L3 and L5 AD studies and in population surveys on AD. The evidence of the presented study should be validated and compared to other questionnaires on AD in different countries around the globe.     

Temporal fluctuations in driving demand: The effect of traffic complexity on subjective measures of workload and driving performance

Traffic density has been shown to be a factor of traffic complexity which influences driver workload. However, little research has systematically varied and examined how traffic density affects workload in dynamic traffic conditions. In this driving simulator study, the effects of two dynamically changing traffic complexity factors (Traffic Flow and Lane Change Presence) on workload were examined. These fluctuations in driving demand were then captured using a continuous subjective rating method and driving performance measures. The results indicate a linear upward trend in driver workload with increasing traffic flow, up to moderate traffic flow levels. The analysis also showed that driver workload increased when a lane change occurred in the drivers’ forward field of view, with further increases in workload when that lane change occurred in close proximity. Both of these main effects were captured via subjective assessment and with driving performance parameters such as speed variation, mean time headway and variation in lateral position. Understanding how these traffic behaviours dynamically influence driver workload is beneficial in estimating and managing driver workload. The present study suggests possible ways of defining the level of workload associated with surrounding traffic complexity, which could help contribute to the design of an adaptive workload estimator.