Understanding take-over performance of high crash risk drivers during conditionally automated driving

The monitoring requests on young driver's fatigue and take-over performance in prolonged conditional automated driving

INTRODUCTION

L3 automated vehicles can perform all dynamic driving tasks unless a take-over occurs due to operational limits. This issue is potentially important for young drivers who are vulnerable road users since they have skill deficits and easily evolve into aberrant driving. However, drivers lacking active involvement may be fatigued and drowsy. Previous research indicated that performing a voluntary non-driving-related task (NDRT) could keep drivers alert, but there was no difference in take-over performance with or without NDRT. Providing a monitoring request (MR) before a possible take-over request (TOR) exhibited better take-over performance in temporary automated driving. Therefore, the study aimed to investigate the effects of MR and voluntary NDRT on young drivers' fatigue and performance.

METHOD

Twenty-five young drivers experienced 60 min automated driving on a highway with low traffic density and a TOR prompted due to a collision event. A within-subjects was designed that comprised three conditions: NONE, TOR-only, and MR + TOR. Drivers were allowed to perform a self-paced phone NDRT during automated driving.

RESULTS

The PERCLOS and blink frequency data showed that playing phones could keep drivers vigilant. The take-over performance on whether taking phone had no difference, but with MRs condition exhibited better take-over performance including the shorter reaction time and the longer TTC. Subjective evaluations also showed the advantages of MRs with more safety, trust, acceptance, and lower workload.

CONCLUSIONS

Taking MRs had a positive effect on relieving fatigue and improving take-over performance. Furthermore, MRs could potentially improve the safety and acceptance of automated driving.

PRACTICAL APPLICATIONS

The MR design can be used in the automotive industry to ensure the safest interfaces between fatigue drivers and automation systems.

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.

A Study on the Entire Take-Over Process-Based Emergency Obstacle Avoidance Behavior

Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system's control. This study aimed to explore the changing trend of the drivers' takeover behavior under the influence of traffic density and take-over budget time for the entire take-over process in emergency obstacle avoidance scenarios. In the driving simulator, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of take-over budget time (3 s and 5 s). A total of 40 drivers were recruited, and each driver was required to complete four simulation experiments. The driver's take-over process was divided into three phases, including the reaction phase, control phase, and recovery phase. Time parameters, dynamics parameters, and operation parameters were collected for each take-over phase in different obstacle avoidance scenarios. This study analyzed the variability of traffic density and take-over budget time with take-over time, lateral behavior, and longitudinal behavior. The results showed that in the reaction phase, the driver's reaction time became shorter as the scenario urgency increased. In the control phase, the steering wheel reversal rate, lateral deviation rate, braking rate, average speed, and takeover time were significantly different at different urgency levels. In the recovery phase, the average speed, accelerating rate, and take-over time differed significantly at different urgency levels. For the entire take-over process, the entire take-over time increased with the increase in urgency. The lateral take-over behavior tended to be aggressive first and then became defensive, and the longitudinal take-over behavior was defensive with the increase in urgency. The findings will provide theoretical and methodological support for the improvement of take-over behavior assistance in emergency take-over scenarios. It will also be helpful to optimize the human-machine interaction system.

Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving

The objective of this study is to examine the effects of visibility and time headway on the takeover performance in L3 automated driving. Both non-critical and critical driving scenarios were considered by changing the acceleration value of the leading vehicle. A driving simulator experiment with 18 driving scenarios was conducted and 30 participants complete the experiment. Based on the data obtained from the experiment, the takeover reaction time, takeover control time, and takeover responses were analyzed. The minimum Time-To-Collision (Min TTC) was used to measure the takeover risk level and a binary logit model for takeover risk levels was estimated. The results indicate that the visibility distance (VD) has no significant effects on the takeover control time, while the time headway (THW) and the acceleration of the leading vehicle (ALV) could affect the takeover control time significantly; most of the participants would push the gas pedal to accelerate the ego vehicle as the takeover response under non-critical scenarios, while braking was the dominant takeover response for participants in critical driving scenarios; decreasing the TCT and taking the appropriate takeover response would reduce the takeover risk significantly, so it is suggested that the automation system should provide the driver with the urgency of the situation ahead and the tips for takeover responses by audio prompts or the head-up display. This study is expected to facilitate the overall understanding of the effects of visibility and time headway on the takeover performance in conditionally automated driving.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study

Road traffic accidents (RTAs) are an ever-existing threat to all road users. Automated vehicles (AVs; SAE Level 3-5) are developed in many countries. They are promoted with numerous benefits such as increased safety yielding less RTAs, less congestion, less greenhouse gas emissions, and the possibility of enabling non-driving related tasks (NDRTs). However, there has been no study which has investigated different NDRT conditions, while comparing participants who experienced a severe RTA in the past with those who experienced no RTA. Therefore, we conducted a driving simulator study (N = 53) and compared two NDRT conditions (i.e., auditory-speech (ASD) vs. heads-up display (HUD)) and an accident (26 participants) with a non-accident group (27; between-subjects design). Although our results did not reveal any interaction effect, and no group difference between the accident and the non-accident group on NDRT, take-over request (TOR), and driving performance, we uncovered for both groups better performances for the HUD condition, whereas a lower cognitive workload was reported for the ASD condition. Nevertheless, there was no difference for technology trust between the two conditions. Albeit we observed higher self-ratings of PTSD symptoms for the accident than for the non-accident group, there were no group differences on depression and psychological resilience self-ratings. We conclude that severe RTA experiences do not undermine NDRT, TOR, and driving performance in a SAE Level 3 driving simulator study, although PTSD symptoms after an RTA may affect the psychological wellbeing.

Effects of non-driving related tasks on readiness to take over control in conditionally automated driving

OBJECTIVE

At conditionally automated driving, the driver can temporarily engage in non-driving related tasks (NDRTs). However, they must safely take over control when the automated driving system reaches its operation limit. Thus, understanding the effects of the NDRTs on driver take-over performance is essential. The present work investigates the effects of various NDRTs on motor readiness in take-over scenarios during conditionally automated driving.

METHODS

Three driving simulator studies were conducted. 48, 49, and 22 participants were recruited in three experiments, respectively. The participants were distracted by different NDRTs (everyday task in Experiment 1, arrow task in Experiment 2, and SuRT in Experiment 3) on a tablet mounted in the vehicle. The everyday task included reading the news and watching a video, and the arrow task included a set of arrow matrices presented to the participants in sequence. The time budgets in Experiment 1 included 3 s, 4 s, and 5 s, and the time budgets in Experiment 2 and 3 included 5 s and 7 s. A take-over request (TOR) warning was issued in the automated driving condition when the participants encountered a broken-down car in front. The participants must regain control of the vehicle with the given time budget. The hands-on time was evaluated, measuring the time from the TOR until the hands touch the steering wheel.

RESULTS

The task (arrow task and SuRT), time budget (5 s and 7 s), and gender did not affect the hands-on time. However, the hands-on time for the drivers with the everyday task was significantly shorter than that for the drivers with the arrow task in the 5 s time budget.

CONCLUSIONS

In conditionally automated driving, the arrow task and SuRT imposed a similar workload on readiness to take over control. Compared to the everyday task, the engagement in the arrow tasks consumed more workload on readiness to take over control.

Drivers' visual-distracted take-over performance model and its application on adaptive adjustment of time budget

There are certain situations that automated driving (AD) systems are still unable to handle, preventing the implementation of Level 5 AD. Thus, a transition of control, colloquially known as take-over of the vehicle, is required when the system sends a take-over request (TOR) upon exiting the operational design domain (ODD). An adaptive TOR along with good take-over performance requires adjusting the time budget (TB) to drivers' visual distraction state, adhering to a reliable visual-distraction-based take-over performance model. Based on a number of driving simulator experiments, the percentage of face orientation to distraction area (PFODA) and time to boundary at take-over timing (TTBT) were proposed to accurately evaluate the degree of visual distraction based on merely face orientation under naturalistic non-driving related tasks (NDRTs) and to evaluate take-over performance, respectively. In order to elucidate the safety boundary, this study also proposed an algorithm to set a suitable minimum value of the TTBT. Finally, a multiple regression model was built to describe the relationship among PFODA, TB and TTBT along with a corrected coefficient of determination of 0.748. Based on the model, this study proposed an adaptive TB adjustment method for the take-over system.