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

Effect of Takeover Request Time and Warning Modality on Trust in L3 Automated Driving

ObjectiveThis study investigated the effects of four takeover request (TOR) times and seven warning modalities on performance and trust in automated driving on a mildly congested urban road scenario, as well as the relationship between takeover performance and trust.BackgroundTakeover is crucial in L3 automated driving, where human-machine codriving is employed. Establishing trust in takeover scenarios among drivers can enhance the acceptance of autonomous vehicles, thereby promoting their widespread adoption.MethodUsing a driving simulator, data from 28 participants, including collision counts, takeover time (ToT), electrodermal activity (EDA) data, and self-reported trust scores, were collected and analyzed primarily using Generalized Linear Mixed Models (GLMM).ResultsCollisions during the takeover undermined participants' trust in the autonomous driving system. As TOR time increased, participants' trust improved, and the longer TOR time did not lead to participant confusion. There was no significant relationship between warning modality and trust. Furthermore, the combination of three warning modalities did not exhibit a notable advantage over the combination of two modalities.ConclusionThe study examined the effects of TOR time and warning modality on trust, as well as preliminarily explored the potential association between takeover performance, including collisions and ToT, and trust in autonomous driving takeovers.ApplicationResearchers and designers of automotive interactions were given referenceable TOR time and warning modality by this study, which extended the autonomous driving takeover scenarios. These findings contributed to boosting drivers' confidence in transferring control to the automated system.

Comfort in Automated Driving: A Literature Survey and a High-Level Integrative Framework

The advancement of automated vehicle technology and the resulting shift from active driver control to a more passive role introduce previously unexplored factors that influence drivers' comfort. Examples include the vehicle's level of automation and the driver's preferred driving style, trust in the vehicle, and situation awareness. To structure these as a resource that can support future research, we conducted a comprehensive literature review, identifying 51 works that directly or peripherally address comfort in an automated driving context. Most of these works focus on the physical component of comfort, rooted in vehicle dynamics, while only a few consider a broader concept of comfort necessary to encompass a more expansive set of factors. Based on this review, we propose an integrative framework of 27 comfort influencing factors and their interrelationships. We categorize factors into six groups, encompassing the driving environment, vehicle physical features and automation system, and the user's activity, individual characteristics, and understanding of the automated system. These six groups are organized into the three larger categories of environment, vehicle, and user-related considerations. Patterns that emerge from the framework include that: a) some factors primarily influence physical well-being (such as motion forces), b) some contribute to discomfort (automation failures) while others contribute to comfort (secondary activities), c) some are stable and known before the trip (individual characteristics) while others change over time (environment), and d) comfort or discomfort can lead users to change either the relevant factors (level of automation) or their own behavior (secondary activities).

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 influence of a color themed HMI on trust and take-over performance in automated vehicles

Introduction

SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver.

Methods

We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.

Results

Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.

Discussion

Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.

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.

(Mis-)use of standard Autopilot and Full Self-Driving (FSD) Beta: Results from interviews with users of Tesla's FSD Beta

Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.