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

Lin, Qingfeng; Li, Shiqi; Ma, Xiao-Wei; Lu, Guangquan

doi:10.1016/j.aap.2020.105543

Cited by 27 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to unexpected situations, such as accidents or road constructions, the automation system can request to resume the manual control of the vehicle in unplanned emergency TakeOver Requests (TOR). For this purpose, takeover performance has been studied in recent years (Gold et al, 2018;Yang et al, 2018;Lin et al, 2020). Most frequently characterized by the time and quality of takeover, the analysis of takeover performance in critical situations is important and necessary for safety (Gold et al, 2013).…”

Section: Takeover Performancementioning

confidence: 99%

Takeover performance according to the level of disengagement during automated driving

Gallouin

Wang

Beillas

et al. 2022

Proceedings of the 7th International Digital Human Modeling Symposium -

View full text Add to dashboard Cite

Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver's level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform nondriving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver's level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control.Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.

show abstract

Section: Takeover Performancementioning

confidence: 99%

Takeover performance according to the level of disengagement during automated driving

Gallouin

Wang

Beillas

et al. 2022

Proceedings of the 7th International Digital Human Modeling Symposium -

View full text Add to dashboard Cite

show abstract

“…According to the SAE standard, vehicle automation systems are categorized into 6 levels from L0 to L5 (full automation) [ 3 ]. Due to the limitations of technologies, law, and ethics issues, it is believed that realizing full automation on public roads still requires time, while conditional automation (L3) is expected to be widely applied in the near future [ 4 ]. At L3 driving automation, the vehicle is capable of conducting the entire dynamic driving tasks (DDTs) such as monitoring the environment and self-control under limited conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Drivers are not required to monitor the roadway and are able to perform non-driving-related tasks (NDRTs) within the operating design domain (ODD) of the L3 automated system [ 3 ]. When the vehicle is exiting its ODD or encounters an automation system failure, a takeover request (TOR) would be issued by the system, and the driver is required to take over the vehicle control within a limited time during the L3 automated driving to continue with the driving task or avoid any potential accidents [ 4 ]. Although the conditional automation system would bring about many benefits to drivers by allowing drivers to delegate driving tasks to the automated driving system, the safety issue during takeover is the most substantial concern which needs to be addressed when promoting L3 automation vehicles [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…A non-critical scenario means that the TOR takes place in a planned manner when the vehicle is about to leave the ODD areas, such as entering a ramp or rural roads without traffic signs and markings [ 9 ]. Most of the previous studies focused on critical takeover scenarios, often with the driving tasks of avoiding collisions ahead by braking or changing lanes [ 4 , 10 , 11 ]. Factors influencing the safety of takeover process in critical scenarios, such as driver characteristics [ 4 ], time-budget [ 11 ], takeover requests [ 12 ], non-driving related tasks [ 10 ], and road conditions [ 13 ], have been investigated in recent years, and meaningful findings and suggestions were obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the previous studies focused on critical takeover scenarios, often with the driving tasks of avoiding collisions ahead by braking or changing lanes [ 4 , 10 , 11 ]. Factors influencing the safety of takeover process in critical scenarios, such as driver characteristics [ 4 ], time-budget [ 11 ], takeover requests [ 12 ], non-driving related tasks [ 10 ], and road conditions [ 13 ], have been investigated in recent years, and meaningful findings and suggestions were obtained. For the non-critical scenarios, the literature on the takeover issue of L3 automation vehicles is limited [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Peng

Chen

2022

IJERPH

View full text Add to dashboard Cite

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.

show abstract

Exploring the Effects of Request Time, Secondary Task, and Take-Over Mode on Take-Over Performance

Dong

Wang

Chen

et al. 2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

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

Cited by 27 publications

References 32 publications

Takeover performance according to the level of disengagement during automated driving

Takeover performance according to the level of disengagement during automated driving

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

Exploring the Effects of Request Time, Secondary Task, and Take-Over Mode on Take-Over Performance

Contact Info

Product

Resources

About