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

Identifying novice drivers in need of hazard perception ability improvement for takeover performance in Level 3 automated driving

Takeover performance is a crucial constraint on deploying Level 3 automated driving. Not all drivers can adopt appropriate strategies to take over vehicle control during safety-critical situations. The hazard perception abilities of novice drivers may cause individual differences in urgent takeover performance. This research examines the urgent takeover performances of novice drivers with different hazard perception abilities for takeover safety improvement. Forty novice drivers took over in urgent cut-in situations at a driving simulator. The hazard perception tests evaluated their hazard perception abilities. This study formulated moderating effect models based on experimental data. Results indicated that hazard perception ability indirectly affected the significance of the correlation between takeover reaction and steering behaviors. Drivers with improved hazard perception abilities are less likely to turn sharply on the steering wheel. In this study, 39.8% of the participants need to improve their hazard perception abilities. Their z-scores were longer than 0.002 in hazard perception tests. Findings can identify the individuals who need hazard perception training to enhance their takeover performance effectively.

Effects of dual-message tactile sliding takeover requests on takeover performance in an automated driving system

OBJECTIVE

The present study aimed to explore the effects of various tactile takeover requests (TORs) (i.e., tactile sliding TOR and traditional vibration TOR) on the takeover performance in an automated driving system.

METHODS

A tactile sliding motor device was developed to signal the sliding TOR on the seatback of a driving simulator. Twenty-five young drivers were recruited as participants. Four types of TOR patterns were adopted in the study: ipsilateral motor rotation (IR), contralateral motor rotation (CR), ipsilateral and contralateral motor rotation (ICR), and ipsilateral motor vibration (IV). The participants were required to sit on the seat and underwent the automated driving in a low- or high-complexity scenario, then one of the four types of TORs was triggered randomly. The participants were asked to make a lane change using the steering wheel as soon as possible. Objective measures and subjective evaluations were used to assess the takeover performance.

RESULTS

Results showed that the participants exhibited a shorter steering response time and lane change time under the three tactile sliding TORs (compared to the traditional vibration TOR). In the high-complexity scenarios and low-complexity scenarios conditions, different result patterns appeared regarding the maximum lateral acceleration and situational awareness.

CONCLUSION

Our findings suggested that the tactile sliding motor is a promising way to signal a TOR in an automated driving system.

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

OBJECTIVE

This 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.

BACKGROUND

Takeover 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.

METHOD

Using 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).

RESULTS

Collisions 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.

CONCLUSION

The 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.

APPLICATION

Researchers 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.

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.

Drinking and driving: A systematic review of the impacts of alcohol consumption on manual and automated driving performance

INTRODUCTION

Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving.

METHOD

This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model.

RESULTS

The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver's driving experience, drinking habits, and external driving environment play important roles in influencing driving performance.

CONCLUSIONS

Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems.

PRACTICAL APPLICATIONS

Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems.

Quantification of safety improvements and human-machine tradeoffs in the transition to automated driving

The assumption of reduced human error-related crashes with increasing levels of automation in pursuing Level 5 automation lacks empirical evidence. As automation levels rise, human error-induced safety hazards are anticipated to decrease, while machine error-induced hazards will increase. However, a quantitative index capturing this tradeoff is absent. Additionally, theoretical modeling of safety improvements during the transition to automated driving remains unexplored, particularly concerning reducing human error-related hazards. These limitations impede the understanding of safety from human and machine perspectives for Automated Vehicle (AV) specialists and manufacturers. This research addresses these gaps by investigating safety performance associations between human and machine factors using the "Human-Machine conflict reduction ratio" (H/M ratio), a novel metric. The study aims to establish safety improvements related to human errors under various automation levels. Sixty participants completed driving tasks on a driving simulator at Levels 0, 4, 3, and 2. Safety performance measures, including conflict frequency and severity, were computed. As a result, Level 4 exhibits the largest decrease (93.3%) compared to manual driving, followed by Level 2 (70.7%) and Level 3 (40.5%). The H/M ratio measures the tradeoff between reducing human and machine error-induced hazards, with Level 2 demonstrating the highest ratio, followed by Levels 4 and 3. Safety performance is evaluated by considering all possible types of human errors at each automation level. Theoretical models from a human factor's perspective are employed to estimate safety improvements at each level. This research contributes to a comprehensive understanding of safety in the "human-machine cooperative driving" phase, offering insights to AV industry practitioners and stakeholders.

Constructing the behavioral sequence of the takeover process-TOR, behavior characteristics and phases division: A real vehicle experiment

Autonomous driving will still use human-machine co-driving to handle complex situations for a long term, which requires the driver to control the vehicle and avoid hazards by executing appropriate behavioral sequences after takeover prompts. Previous studies focused on the division of static behavioral indicators and major phases in the initial phase of takeover, while lacking the construction of behavioral sequences based on the dynamic changes of behavioral characteristics during the takeover process. This study divides the takeover process in a detailed manner and investigates the impact of audio types on the behavioral sequence at each phase. 20 professional drivers performed the NDRT in autonomous driving mode on real roads, and after receiving audio prompts, they took over the vehicle and performed hazard avoidance maneuvers. The results show that the behavioral characteristics could construct the behavioral sequence of different phases, with the dynamic characteristics of the takeover operation change. In addition, different types of audio prompts will affect the timing of the takeover operation and its driving performance. Choosing different audio prompts or combinations can help improve the effect of taking over the vehicle. This study helps to provide guidance on the design of human-machine interaction for behavior optimization at different phases, so that guiding the driver to take over the vehicle safely and effectively.

Impact of non-driving related tasks while operating automated driving systems (ADS): A systematic review

Automated Driving Systems (ADS) (SAE, 2021), promise improved safety and comfort for drivers. Current technological advances have resulted in increased automation capabilities. However, with the increase in automation capabilities, there is a shift in how drivers interact with their vehicles. Drivers can now temporarily hand over the control of the driving task to ADS under certain conditions. However, with ADS in temporary control of the vehicle, drivers may choose to engage in non-driving related tasks (NDRT). The current capabilities of ADS do not allow drivers to hand over control of the driving task indefinitely. Drivers must remain aware and be ready to take back control if necessary. There is a need to better understand drivers' performance and behaviors when driving with ADS, especially when engaged in NDRTs. This literature review, therefore, aims to understand the state of knowledge on automated vehicle systems and driver distraction. This review was conducted as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies found a significant increase in takeover times while engaging in NDRTs and driving with automation active. Studies also discuss a change in driver's visual attention, with more focus given to NDRTs as compared to the front roadway. The concerning effects of increasing reaction times and decreases in visual attention can be mitigated by using interventions and studies have had success in redirecting drivers attention and reorient them to the task of driving. The review, therefore, includes a discussion of ADS and NDRT engagement and its impact on driving behaviors such as take-over times, visual attention, trust, and workload. Implications on driver safety and performance are discussed in light of this synthesis.