Safety Enhancement of Adaptive Cruise Control Adapted to Driver Eyes-Off State

Advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC), have been recently installed in passenger cars. Although the safety performance of these systems is limited in high-risk scenarios, some drivers overtrust the system and perform secondary tasks. Previous research indicated that drivers using ADAS tend to become distracted compared with manual driving. In contrast, the use of ACC has been reported to reduce the collision rate on highways by about half. This study aimed to clarify the mechanism of the effect of ACC on driver behavior and consequently mitigate accidents. Our previous experiments showed that driver reaction time to perform avoidance behaviors in high-risk scenes is shortened when using ACC, even if the driver is distracted. This paper first aims to elucidate the factors influencing driver risk-avoidance strategies in a potentially critical frontal collision scenario. The hypothesis is that the driver’s perception of tactile vehicle motion, accompanied by the deceleration of ACC active intervention, prompts risk awareness and avoidance. The hypothesis was verified through analysis of driver gaze movement and brake operation behavior in critical scenarios using driving simulator experiments. Based on the obtained results, the advanced driver assistance system longitudinal control laws adapted to the driver’s eyes-off state are proposed based on the high-risk scenarios. Finally, the driver acceptance and ability to reduce the risk of the proposed system were quantitatively evaluated using a driving simulator.

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Publication Details

Journal
Journal of Robotics and Mechatronics
Published
2025-10-19
DOI
https://doi.org/10.20965/jrm.2025.p1162
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

Safety Enhancement of Adaptive Cruise Control Adapted to Driver Eyes-Off State

Fumio Sugaya, Yohei Fujinami, Toshinori Okita, Pongsathorn Raksincharoensak et al.
Journal of Robotics and Mechatronics
Human-Automation Interaction and Safety
article

Safety Enhancement of Adaptive Cruise Control Adapted to Driver Eyes-Off State

Fumio Sugaya, Yohei Fujinami, Toshinori Okita, Pongsathorn Raksincharoensak, Shintaro Inoue, N. Arai, Masaaki Uechi, Shounosuke Tsujiide
article en

Abstract

Advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC), have been recently installed in passenger cars. Although the safety performance of these systems is limited in high-risk scenarios, some drivers overtrust the system and perform secondary tasks. Previous research indicated that drivers using ADAS tend to become distracted compared with manual driving. In contrast, the use of ACC has been reported to reduce the collision rate on highways by about half. This study aimed to clarify the mechanism of the effect of ACC on driver behavior and consequently mitigate accidents. Our previous experiments showed that driver reaction time to perform avoidance behaviors in high-risk scenes is shortened when using ACC, even if the driver is distracted. This paper first aims to elucidate the factors influencing driver risk-avoidance strategies in a potentially critical frontal collision scenario. The hypothesis is that the driver’s perception of tactile vehicle motion, accompanied by the deceleration of ACC active intervention, prompts risk awareness and avoidance. The hypothesis was verified through analysis of driver gaze movement and brake operation behavior in critical scenarios using driving simulator experiments. Based on the obtained results, the advanced driver assistance system longitudinal control laws adapted to the driver’s eyes-off state are proposed based on the high-risk scenarios. Finally, the driver acceptance and ability to reduce the risk of the proposed system were quantitatively evaluated using a driving simulator.

Journal of Robotics and MechatronicsVol. 37(5)
Toyota Motor Corporation (Japan) (JP), Tokyo University of Agriculture and Technology (JP)
Openalex Percentile: Top 66%
Human-Automation Interaction and Safety
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