An interview study exploring Tesla drivers' behavioural adaptation

Predicting driver's takeover time based on individual characteristics, external environment, and situation awareness

The driver's takeover time is crucial to ensure a safe takeover transition in conditional automated driving. The study aimed to construct a prediction model of driver's takeover time based on individual characteristics, external environment, and situation awareness variables. A total of 18 takeover events were designed with scenarios, non-driving-related tasks, takeover request time, and traffic flow as variables. High-fidelity driving simulation experiments were carried out, through which the driver's takeover data was obtained. Fifteen basic factors and three dynamic factors were extracted from individual characteristics, external environment, and situation awareness. In this experiment, these 18 factors were selected as input variables, and XGBoost and Shapely were used as prediction methods. A takeover time prediction model (BM + SA model) was then constructed. Moreover, we analyzed the main effect of input variables on takeover time, and the interactive contribution made by the variables. And in this experiment, the 15 basic factors were selected as input variables, and the basic takeover time prediction model (BM model) was constructed. In addition, this study compared the performance of the two models and analyzed the contribution of input variables to takeover time. The results showed that the goodness of fit of the BM + SA model (Adjusted_R2) was 0.7746. The XGBoost model performs better than other models (support vector machine, random forest, CatBoost, and LightBoost models). The relative importance degree of situation awareness variables, individual characteristic variables, and external environment variables to takeover time gradually reduced. Takeover time increased with the scan and gaze durations and decreased with pupil area and self-reported situation awareness scores. There was also an interaction effect between the variables to affect takeover time. Overall, the performance of the BM + SA model was better than that of the BM model. This study can provide support for predicting driver's takeover time and analyzing the mechanism of influence on takeover time. This study can provide support for the development of real-time driver's takeover ability prediction systems and optimization of human-machine interaction design in automated vehicles, as well as for the management department to evaluate and improve the driver's takeover performance in a targeted manner.

Habits, attitudes, and expectations of regular users of partial driving automation systems

INTRODUCTION

Little is known about regular users' perceptions of partial (Level 2) automation or how those perceptions affect behind-the-wheel behavior.

METHOD

A mixed mode (phone and online) survey explored the habits, expectations, and attitudes among regular users of General Motors Super Cruise (n = 200), Nissan/Infiniti ProPILOT Assist (n = 202), and Tesla Autopilot (n = 202).

RESULTS

All three groups reported being more likely to engage in non-driving-related activities while using their systems than while driving unassisted. Super Cruise and Autopilot users especially were more likely to report engaging in activities that involved taking their hands off the wheel or their eyes off the road. Many Super Cruise and Autopilot users also said they could perform secondary (non-driving-related) tasks better and more often while using their systems, while fewer ProPILOT Assist users shared this opinion. Super Cruise users were most likely and ProPILOT Assist users least likely to think that secondary activities were safer to perform while using their systems. While some drivers said they found user safeguards (e.g., attention reminders, lockouts) annoying and tried to circumvent them, most people said they found them helpful and felt safer with them. Large percentages of users (53% Super Cruise, 42% Autopilot and 12% ProPILOT Assist) indicated they were comfortable treating their systems as self-driving.

CONCLUSIONS

Some regular users have a poor understanding of their technology's limits. System design appears to contribute to user perceptions and behavior. However, owner populations also differ, which means habits, attitudes, and expectations may not generalize. Most people value user safeguards, but some implementations may not be effective for everyone.

PRACTICAL APPLICATIONS

Multifaceted, proactive user-centric safeguards are needed to shape proper behavior and understanding about drivers' roles and responsibilities while using partial driving automation.

The relationship between aggressive driving and driver performance: A systematic review with meta-analysis

Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver's performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver's control performance among the general driver population in four-wheeled passenger vehicles and published with full text in English. These 34 selected studies involved 1731 participants in total. By examining the selected 34 studies, the measures relating to vehicle speed (e.g., mean speed, n = 22), lateral control (e.g., lane deviation, n = 17) and driving errors (e.g., violation of traffic rules, n = 12) were reported most frequently with a significant difference observed between aggressive driving and driving in the control group. The result of the meta-analysis indicates that the aggressive driving behaviour would have 1) a significantly faster speed than the behaviour in the control group with an increase of 5.32 km/h (95% confidence interval, [3.27, 7.37] km/h) based on 8 studies with 639 participants in total; 2) 2.51 times more driving errors (95% confidence interval, [1.32, 3.71] times) than the behaviour in the control group, based on 5 studies with 136 participants in total. This finding can be used to support the identification and quantification of aggressive driving behaviour, which could form the basis of an in-vehicle aggressive driving monitoring system.

Driver response and recovery following automation initiated disengagement in real-world hands-free driving

OBJECTIVE

Advanced driver assistance systems are increasingly available in consumer vehicles, making the study of drivers' behavioral adaptation and the impact of automation beneficial for driving safety. Concerns over driver's being out-of-the-loop, coupled with known limitations of automation, has led research to focus on time-critical, system-initiated disengagements. This study used real-world data to assess drivers' response to, and recovery from, automation-initiated disengagements by quantifying changes in visual attention, vehicle control, and time to steady-state behaviors.

METHODS

Fourteen drivers drove for one month each a Cadillac CT6 equipped with Super Cruise (SC), a partial automation system that, when engaged, enables hands-free driving. The vehicles were instrumented with data acquisition systems recording driving kinematics, automation use, GPS, and video. The dataset included 265 SC-initiated disengagements identified across 5,514 miles driven with SC.

RESULTS

Linear quantile mixed-effects models of glance behavior indicated that following SC-initiated disengagement, the proportions of glances to the Road decreased (Q50_Before=0.91, Q50_After=0.69; Q85_Before=1.0, Q85_After=0.79), the proportions of glances to the Instrument Cluster increased (Q50_Before=0.14, Q50_After=0.25; Q85_Before=0.34, Q85_After=0.45), and mean glance duration to the Road decreased by 4.86 sec in Q85. Multinomial logistic regression mixed-models of glance distributions indicated that the number of transitions between glance locations following disengagement increased by 43% and that glances were distributed across fewer locations. When driving hands-free, take over time was significantly longer (2.4 sec) compared to when driving with at least one hand on the steering wheel (1.8 sec). Analysis of moment-to-moment distributional properties of visual attention and steering wheel control following disengagement indicated that on average it took drivers 6.1 sec to start the recovery of glance behavior to the Road and 1.5 sec for trend-stationary proportions of at least one hand on the steering wheel.

CONCLUSIONS

Automation-initiated disengagements triggered substantial changes in driver glance behavior including shorter on-road glances and frequent transitions between Road and Instrument Cluster glance locations. This information seeking behavior may capture drivers' search for information related to the disengagement or the automation state and is likely shaped by the automation design. The study findings can inform the design of more effective driver-centric information displays for smoother transitions and faster recovery.

Do driver's characteristics, system performance, perceived safety, and trust influence how drivers use partial automation? A structural equation modelling analysis

The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their automated car with speed (ACC) and steering assist (LKA) at least 1–2 times a week, and 84 and 92% activate LKA and ACC at least occasionally. Respondents positively rated the performance of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA). ACC was rated higher than LKA and detection of lead vehicles and lane markings was rated higher than smooth control for ACC and LKA, respectively. Respondents reported to primarily disengage (i.e., turn off) partial automation due to a lack of trust in the system and when driving is fun. They rarely disengaged the system when they noticed they become bored or sleepy. Structural equation modelling revealed that trust had a positive effect on driver’s propensity for secondary task engagement during partially automated driving, while the effect of perceived safety was not significant. Regarding driver’s characteristics, we did not find a significant effect of age on perceived safety and trust in partial automation. Neuroticism negatively correlated with perceived safety and trust, while extraversion did not impact perceived safety and trust. The remaining three personality dimensions ‘openness’, ‘conscientiousness’, and ‘agreeableness’ did not form valid and reliable scales in the confirmatory factor analysis, and could thus not be subjected to the structural equation modelling analysis. Future research should re-assess the suitability of the short 10-item scale as measure of the Big-Five personality traits, and investigate the impact on perceived safety, trust, use and use of automation.

Drivers don't need to learn all ADAS limitations: A comparison of limitation-focused and responsibility-focused training approaches

Expecting drivers to learn and remember numerous limitations may not be a practical approach to training for advanced driver assistance systems (ADAS), particularly for self-initiated training in the absence of formal training requirements. One alternative is focusing on the importance of the driver remaining engaged in the driving task (responsibility-focused approach). We investigated the effects of two training videos (responsibility-focused and limitation-focused) on reliance intention, trust, and ADAS knowledge. In a remote study, participants (N = 61) watched dashcam clips (8 that require takeover, 8 no takeover) and for each clip, they reported whether they would manually intervene and their trust in ADAS (assessing situational reliance intention and trust, respectively). Participants also completed a questionnaire that included items measuring ADAS knowledge. Responses were collected at three stages: pre-training, post-training, and a follow-up session (minimum four weeks later). There were no significant differences between approaches in terms of knowledge of situations in which ADAS would not work, appropriate situational reliance intention, or trust in takeover scenarios. Compared to the responsibility-focused video, the limitation-focused video was associated with lower trust in no takeover scenarios and negative bias at post-training (i.e., bias towards reporting that ADAS would not work for the knowledge questionnaire and bias towards taking manual control/not using ADAS for the dashcam clips). Given the limited differences between training approaches and potential drawbacks of the limitation-focused approach, our results suggest that the responsibility-focused training approach is worth exploring further.

When Self-Driving Fails: Evaluating Social Media Posts Regarding Problems and Misconceptions about Tesla’s FSD Mode

With the recent deployment of the latest generation of Tesla’s Full Self-Driving (FSD) mode, consumers are using semi-autonomous vehicles in both highway and residential driving for the first time. As a result, drivers are facing complex and unanticipated situations with an unproven technology, which is a central challenge for cooperative cognition. One way to support cooperative cognition in such situations is to inform and educate the user about potential limitations. Because these limitations are not always easily discovered, users have turned to the internet and social media to document their experiences, seek answers to questions they have, provide advice on features to others, and assist other drivers with less FSD experience. In this paper, we explore a novel approach to supporting cooperative cognition: Using social media posts can help characterize the limitations of the automation in order to get information about the limitations of the system and explanations and workarounds for how to deal with these limitations. Ultimately, our goal is to determine the kinds of problems being reported via social media that might be useful in helping users anticipate and develop a better mental model of an AI system that they rely on. To do so, we examine a corpus of social media posts about FSD problems to identify (1) the typical problems reported, (2) the kinds of explanations or answers provided by users, and (3) the feasibility of using such user-generated information to provide training and assistance for new drivers. The results reveal a number of limitations of the FSD system (e.g., lane-keeping and phantom braking) that may be anticipated by drivers, enabling them to predict and avoid the problems, thus allowing better mental models of the system and supporting cooperative cognition of the human-AI system in more situations.

An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.

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

This study explored the possibility of applying personalized takeover requests (TORs) in an automated driving system (ADS), which required drivers to regain control when the system reached its limits. A driving simulator experiment was conducted to investigate how speech-based TOR voices impacted driver performance in takeover scenarios with two lead time conditions in conditionally automated driving (level 3). Eighteen participants drove in three sessions, with each session having a different TOR voice (a synthesized male voice, a synthesized female voice, and a significant other voice). Two scenarios with a lead time of 5 s and two scenarios with a lead time of 12 s were provided per session. The driver takeover time and quality data were collected. A follow-up interview was conducted to gain a clearer understanding of the drivers' psychological feelings about each TOR voice during takeovers. Changes in takeover time and takeover quality caused by TOR voices were similar in both lead time conditions, except for the lateral acceleration. The synthesized male voice led to a larger maximum lateral acceleration than the other two voices in the 5 s condition. Interestingly, most drivers preferred choosing the synthesized female voice for future takeovers and showed negative attitudes toward the significant other voice. Our results implied that choosing TOR voices should consider the drivers' daily voice-usage habits as well as specific context of use, and personalized TOR voices should be incorporated into the ADS prudently.

Understanding sexual activity while driving as a form of distracted driving

Sexual activity while driving has been reported in emerging research. Sexual activity while driving is a form of distracted driving because it includes an individual (the driver) who deviates resources from the primary task (driving) towards a secondary task (sexual activity). However, most of our current knowledge about the range of sexual activities while driving is based on self-reported data or media reports. Thus, an in-depth understanding of sexual activities while driving and their interactions with non-sexual driving behaviours and vehicle control is missing. Additionally, there is limited information on the context of where sexual activities while driving occurs and the influence of factors such as the environment, the vehicle, interactions with other road users, and other in-vehicle distractions. To cover this gap, a content analysis of sexually explicit media (SEM) was conducted on a sample of 270 videos depicting real driving. We conducted descriptive analyses and used decision tree analysis to explore the association between sexual activities while driving and their interactions with non-sexual driving behaviours and vehicle control. The videos portrayed a naturalistic driving situation of a driver of a moving vehicle engaging in sexual activity. The results show that when engaging in sexual activity, drivers do not present safe vehicle control. Sexual activity imposes additional cognitive, physical, and visual demands on the driver, thereby decreasing safety. Similar to other distractions, drivers engaging in sexual activity while driving appear to mitigate risks. Concerning the potential for legal sanctions, it appears that drivers may attempt to conceal sexual activity by reducing their visible nudity and minimising interactions with other road users. Finally, mobile phones and cameras appear to interact with sexual activities while driving, by imposing potential restrictions on the range of sexual activities. Implications for policymakers and practitioners are discussed.

Clinical Report: Experiences of a Driver with Vision Impairment when Using a Tesla Car

SIGNIFICANCE

Assisted and autonomous driving technologies may be a new paradigm shift for the driving rehabilitation field to enable less restricted driving, increase driving confidence and maintain driving safety for drivers with vision impairment.

PURPOSE

To document how a driver with vision impairment uses assistance and automation systems in a Tesla car based on real-world experiences of using these technologies.

METHODS

A 53-year-old male with Stargardt's Disease and 20/182 visual acuity self-explored use of driver assistance technologies which resulted in purchasing a Tesla Model Y with the full self-driving package in 2020. Two semi-structured interviews were administered to quantify the driver's driving habits, adaptive strategies, use of the assisted and semi-autonomous driving features in the Tesla car, and use of his bioptic telescope.

RESULTS

When driving a Tesla car, the patient developed new driving strategies by co-driving with different assisted and semi-autonomous functions (e.g., Traffic Aware Cruise Control, Traffic Light and Stop Sign Control, Autopilot) in different road environments. He shifted his main task from active driving to supervising the car automation systems in most driving situations. He also integrated a new use of his bioptic telescope to support him with monitoring the road environment before granting permission to the automated systems for car maneuver changes. The patient reported that driving confidence greatly increased and that he is able to drive more often and in situations that he would otherwise avoid due to difficulties related to his vision.

CONCLUSIONS

This clinical report demonstrates how assisted and semi-autonomous driving systems in a Tesla car were used to support daily driving by a driver with vision impairment. Co-driving with these systems allows him to confidently drive more often and to avoid less situations than he used to.

Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M= 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M= 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.

A model for naturalistic glance behavior around Tesla Autopilot disengagements

OBJECTIVE

We present a model for visual behavior that can simulate the glance pattern observed around driver-initiated, non-critical disengagements of Tesla's Autopilot (AP) in naturalistic highway driving.

BACKGROUND

Drivers may become inattentive when using partially-automated driving systems. The safety effects associated with inattention are unknown until we have a quantitative reference on how visual behavior changes with automation.

METHODS

The model is based on glance data from 290 human initiated AP disengagement epochs. Glance duration and transition were modelled with Bayesian Generalized Linear Mixed models.

RESULTS

The model replicates the observed glance pattern across drivers. The model's components show that off-road glances were longer with AP active than without and that their frequency characteristics changed. Driving-related off-road glances were less frequent with AP active than in manual driving, while non-driving related glances to the down/center-stack areas were the most frequent and the longest (22% of the glances exceeded 2 s). Little difference was found in on-road glance duration.

CONCLUSION

Visual behavior patterns change before and after AP disengagement. Before disengagement, drivers looked less on road and focused more on non-driving related areas compared to after the transition to manual driving. The higher proportion of off-road glances before disengagement to manual driving were not compensated by longer glances ahead.

APPLICATION

The model can be used as a reference for safety assessment or to formulate design targets for driver management systems.

User Monitoring in Autonomous Driving System Using Gamified Task: A Case for VR/AR In-Car Gaming

Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

Public acceptance and perception of autonomous vehicles: a comprehensive review

Autonomous vehicles (AVs) or self-driving cars have the potential to provide many benefits such as improving mobility and reducing the energy and emissions consumed, travel time, and vehicle ownership. Thus, in the last few years, both research and industry have put significant efforts to develop AVs. However, laws and regulations are not ready yet for this switch and the legal sector is unable to take the lead but follow the development of AVs. Besides, the social acceptance is considered as a main key factor for the success of any new technology. Despite the enthusiastic speculation of AVs, little is known about the public acceptance and perception of the AVs technology or the factors that influence the public acceptance. This paper reviews the previous studies that focuses on testing the public acceptance and perception of AVs and sketches out the main trends in this area to provide some directions and recommendations for the future. This paper focuses on the influence of safety, ethics, liability, regulations, and the recent pandemic on the public acceptance of AVs.

Drivers' gap acceptance behaviours at intersections: A driving simulator study to understand the impact of mobile phone visual-manual interactions

Mobile phone use is often considered to be the main source of distraction on the road. Gap acceptance at intersections is a frequent and complex driving task that requires high visual attention from drivers. This study aims to investigate the effect of mobile phone use on the gap acceptance manoeuvre at intersections. Different mobile phone use positions, intersection type, gap size and driver characteristics were considered in the study. A total of 41 licenced drivers drove in an advanced driving simulator in three phone use conditions: baseline (no phone use), using the phone under the steering wheel (covert) and using the phone above the steering wheel (overt). Drivers drove the simulator three times and experienced two intersection types (straight-forward vs. left-turn) and two gap sizes (4 s vs. 7 s) during each drive. A parametric accelerated failure time (AFT) duration model was developed to evaluate the intersection crossing completion time of drivers. The results showed no significant difference of gap acceptance behaviours between the two phone use positions. The distraction task did not affect drivers' gap acceptance decision, but it increased the crossing completion time by over 10 % compared to baseline. Besides, drivers behaved conservatively at intersections while using a mobile phone, such as adopting a larger deceleration, waiting a longer time, and mainting a larger distance to the front vehicle, etc. However, these compensational behaviours were not helpful in improving the intersection traffic situation regarding both safety and efficiency. Intersection type and gap size were both significant factors of gap acceptance decision and crossing completion time. Additionally, younger drivers were more likely to accept a gap than older drivers, and female drivers spent longer time to cross the intersection than males.

Drivers adapt - Be prepared for It!

Behavioral adaptation refers to the change in road user behavior in response to new conditions (Kulmala & Rämä, 2013). Behavioral adaptation can improve safety, but it can also reduce or even eliminate anticipated safety benefits of many well-intentioned road safety countermeasures. To expect driver behavior to remain the same after the implementation of a change in the road, vehicle, or driving environment, is naïve. Empirical studies that do not consider the full range of behavior affected by a countermeasure may similarly overlook the consequences of behavioral adaptation. This paper considers a number of examples of driver safety countermeasure implementation where unexpected results occurred and behavioral adaptation was the likely culprit. These examples are drawn from highway design, traffic control device design, vehicle countermeasures, enforcement countermeasures, driver education countermeasures and impaired driving policies. A previously presented inventory of characteristics to consider when estimating the likelihood for behavioral adaptation (Rudin-Brown et al., 2013) is expanded and presented within the context of the Qualitative Model of Behavioral Adaptation (Rudin-Brown & Noy, 2002; Rudin-Brown, 2010), in the hopes of addressing the question "When can we anticipate the safety effect of a treatment, and when not?"

User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone.