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

Investigating looming tactile takeover requests with various levels of urgency in automated vehicles

Designing an effective takeover request (TOR) in conditionally automated vehicles is crucial to ensure driving safety when the system reaches its limit. In our study, we aimed to investigate the effects of looming tactile TORs (whose urgency is dynamically mapped to the situation's criticality as the vehicle approaches the upcoming obstacle) on takeover performance and subjective experience compared with conventional non-looming TORs (several tactile pulses with consistent inter-pulse intervals). In addition, the impact of the TOR urgency level (with urgency levels matched or unmatched to the situation's criticality) was considered. A total of 30 participants were recruited for this study. They were first asked to map the urgency of tactile signals to the criticality of takeover situations with various times to collision according to the recorded video clips. The looming TORs were constructed based on these mapping results. Then, a simulated driving experiment, employing a within-subject design, was conducted to explore the effects of the tactile TOR type (looming vs. non-looming) and urgency level (less urgency vs. matched urgency vs. greater urgency) on takeover performance and drivers' subjective experience. The results showed that the looming TOR can lead to a shorter takeover time and less maximum lateral acceleration compared with the non-looming TOR. Drivers also rated the looming TOR as more useful. Therefore, the looming TOR has great application potential for enhancing driving safety in automated vehicles. In addition, we found that as the TOR's level of urgency increased, the takeover time decreased. However, the TOR with an urgency level matched to the situation's criticality received higher usefulness and satisfaction ratings, suggesting that there was an important trade-off between the advantage of high-urgency TORs in speeding up driver responses and its cost of a poor experience. The findings of our study shed some light on the design and implementation of the takeover warning system for related practitioners.

Driver Situation Awareness for Regaining Control from Conditionally Automated Vehicles: A Systematic Review of Empirical Studies

OBJECTIVE

An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human-vehicle interfaces (HVIs) and the selection of SA assessment methodologies.

BACKGROUND

Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking.

METHOD

A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion.

RESULTS

A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers.

CONCLUSION

The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs.

APPLICATION

This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

Post-Takeover Proficiency in Conditionally Automated Driving: Understanding Stabilization Time with Driving and Physiological Signals

In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experiments. By analyzing both driving and physiological signals, we investigate the time required for the driver to regain full control and adapt to the dynamic driving task following automation. Our findings show that the stabilization time varies between measured parameters. While the drivers achieved driving-related stabilization (winding, speed) in eight to ten seconds, physiological parameters (heart rate, phasic skin conductance) exhibited a prolonged response. By elucidating the temporal and cognitive dynamics underlying the stabilization process, our results pave the way for the development of more effective and user-friendly automated driving systems, ultimately enhancing safety and driving experience on the roads.

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.

Getting back in the loop: Does autonomous driving duration affect driver's takeover performance?

The level 3 autonomous driving function allows the driver to perform non-driving-related tasks such as watching movies or reading while the system manages the driving task. However, when a difficult situation arises, the driver is requested to return to the loop of control. This switching from driver to passenger then back to driver may modify the driving paradigm, potentially causing an out-of-the-loop state. We tested the hypothesis of a linear (progressive) impact of various autonomous driving durations: the longer the level 3 autonomous function is used, the poorer the driver's takeover performance. Fifty-two participants were divided into 4 groups, each group being assigned a specific period of autonomous driving (5, 15, 45, or 60 min), followed by a takeover request with a time budget of 8.3 s. Takeover performance was assessed over two successive drives via reaction times and manual driving metrics (trajectories). The initial hypothesis (linearity) was not confirmed: there was a nonlinear relationship between autonomous driving duration and takeover performance, with one duration (15 min) appearing safer overall and mixed performance within groups. Repetition induced a major change in performance during the second drive, indicating rapid adaptation to the situation. The non-driving-related task appears critical in several respects (dynamics, content, driver interest) to proper use of level 3 automation. All this supports previous research prompting reservations about the prospect of car driving becoming like train travel.

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.

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.