Modeling takeover behavior in level 3 automated driving via a structural equation model: Considering the mediating role of trust

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.

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles

OBJECTIVE

This study develops a computational model to predict drivers' response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs).

BACKGROUND

Previous research has modeled drivers' takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking.

METHOD

A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists.

RESULTS

Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting.

CONCLUSION

The current model can successfully predict drivers' response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs.

APPLICATION

This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance.

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.

Understanding trust calibration in automated driving: the effect of time, personality, and system warning design

Under the human-automation codriving future, dynamic trust should be considered. This paper explored how trust changes over time and how multiple factors (time, trust propensity, neuroticism, and takeover warning design) calibrate trust together. We launched two driving simulator experiments to measure drivers' trust before, during, and after the experiment under takeover scenarios. The results showed that trust in automation increased during short-term interactions and dropped after four months, which is still higher than pre-experiment trust. Initial trust and trust propensity had a stable impact on trust. Drivers trusted the system more with the two-stage (MR + TOR) warning design than the one-stage (TOR). Neuroticism had a significant effect on the countdown compared with the content warning.Practitioner summary: The results provide new data and knowledge for trust calibration in the takeover scenario. The findings can help design a more reasonable automated driving system in long-term human-automation interactions.

Where drivers are looking at during takeover: Implications for safe takeovers during conditionally automated driving

OBJECTIVE

Safety has become one of the primary concerns of level 3 automated driving, especially during the takeover process. Since most studies have focused on impacts of various factors on takeover performance of drivers, there seems to be a gap between the causes of crashes and the desired means to mitigate their occurrence and consequences. Hence, the main objective of this study is to extract from crash data during takeovers drivers' patterns of gaze behaviors and maneuvers and then utilize them to extract some guidance on human-machine-interface design to enhance safety and acceptability of automated driving.

METHODS

A study involving 27 subjects was conducted on a high-fidelity driving simulator with a Steward motion platform of six degrees of freedom. Each subject participated in 6 takeover scenarios with a lead time of 5 s and different duration of monitoring (DoM), with their maneuvers recorded by the system and eye gazes recorded by the Smart Eye Pro and Smart Recorder. Crash data collected during the takeover process were then utilized for the analysis.

RESULTS

From 132 valid takeovers collected from 23 out of the 27 participants, 15 crashes were recorded. Based on which, five typical patterns of unsafe behaviors were recognized that may have caused the crashes, denoted as Type I to Type V, respectively. Besides, it appears that even if drivers were given more time to observe the surroundings, i.e., longer DoM, the number of crashes has not decreased as anticipated. Therefore, what is more important seems to be drivers' gaze behaviors and maneuvers shortly after TOR.

CONCLUSIONS

For takeovers to be safe, good cooperations between drivers' gaze behaviors and maneuvers are essential. Overall, it seems that in emergent situations that require takeovers, some drivers have difficulty in allocating attentions reasonably, which appears to have less to do with the time left for drivers to observe the surroundings. While designing HMIs, we may as well consider providing enough information to guide drivers according to drivers' states and maneuvers at the time to improve safety of takeovers in emergent situations, and more importantly, to provide the information timely and effectively.

Driving Aggressively or Conservatively? Investigating the Effects of Automated Vehicle Interaction Type and Road Event on Drivers' Trust and Preferred Driving Style

OBJECTIVE

This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events.

BACKGROUND

The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation.

METHODS

Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors.

RESULTS

Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts.

CONCLUSION

Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles.

APPLICATION

Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.

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.

A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving

The evaluation of take-over performance and take-over safety performance is critical to improving the take-over performance of conditionally automated driving, and few studies have attempted to evaluate take-over safety performance. This study applied a binary logistic model to construct a take-over safety performance evaluation model. A take-over driving simulator was established, and a take-over simulation experiment was carried out. In the experiment, data were collected from 15 participants who took over the vehicle and performed emergency evasive maneuvers while performing non-driving-related task (NDRT). Then, to calibrate the abnormal trajectory, the Kalman filter is adopted to filter the disturbed vehicle positioning data and the belief rule-based (BRB) method is proposed to warn irregular driving behavior. The results revealed that the accident rate of male participants is higher than that of female participants in the three frequency take-over experiment, and the overall driving performance of female participants is higher than that of male participants. Meanwhile, medium and high take-over frequencies have a significant effect on the prevention of vehicle collisions. In the take-over safety performance evaluation model, the minimum time to collision (TTC) of 2.3 s is taken as the boundary between the dangerous group and the safety group, and the model prediction accuracy rate is 87.7%. In sum, this study enriches existing research on the safety performance evaluation of conditionally automated driving take-over and provides important implications for the design of driving simulators and the performance and safety evaluation of human-machine take-over.

The effects of takeover request lead time on drivers' situation awareness for manually exiting from freeways: A web-based study on level 3 automated vehicles

Conditional automation systems allow drivers to turn their attention away from the driving task in certain scenarios but still require drivers to gain situation awareness (SA) upon a takeover request (ToR) and resume manual control when the system is unable to handle the upcoming situation. Unlike time-critical takeover situations in which drivers must respond within a relatively short time frame, the ToRs for non-critical events such as exiting from a freeway can be scheduled way ahead of time. It is unknown how the ToR lead time affects driver SA for resuming manual control and when to send the ToR is most appropriate in non-critical takeover events. The present study conducted a web-based, supervised experiment with 31 participants using conditional automation systems in freeway existing scenarios while playing a mobile game. Each participant experienced 12 trials with different ToR lead times (6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 45, and 60 s) for exiting from freeways in a randomized order. Driver SA was measured by using a freeze probe technique in each trial when the participant pressed the spacebar on the laptop to simulate the takeover action. Results revealed a positive effect of longer ToR lead times on driver SA for resuming control to exit from freeways and the effect leveled off at the lead time of 16-30 s. The participants tended to postpone their takeover actions further when they were given a longer ToR lead time and it did not level off up to 60 s. Nevertheless, not all drivers waited till the last moment to take over AVs even though they did not get sufficient SA. The ToR lead time of 16-30 s was recommended for better SA; and it could be narrowed down to 25-30 s if considering the subjective evaluations on takeover readiness, workload, and trust. The findings provide implications for the future design of conditional automation systems used for freeway driving.