Study on the influence factors of takeover behavior in automated driving based on survival analysis

Chen, Haolin; Zhao, Xiaohua; Li, Zhenlong; Li, Haijian; Gong, Jianguo; Wang, Qiuhong

doi:10.1016/j.trf.2023.04.012

Cited by 8 publications

(2 citation statements)

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“…Three drivers (two males and one female) from the previous experiment volunteered to carry on the experiment for 1000 sampling instances. The performance of the driver status classification model is verified using the accuracy in Equation (5). Figure 14 shows the real time evaluation results for each driver, and Figure 15 shows the highest accuracy results for 100 sampling instances during road monitoring.…”

Section: Driver Status Classification Model Evaluation In Real Timementioning

confidence: 99%

“…If the driver takes over control while being distracted or not concentrating, they can take over unstably by applying unnecessary steering or acceleration/brake pedal inputs, which can lead to accidents caused by driver negligence. Previous research has confirmed that takeover times differ depending on driver age and tasks (for example, watching a video, listening to music, or playing mobile games) [1][2][3][4][5]. Although the driver status affects the ability to take control, the current ADAS-based autonomous driving assistance system performs assist driving or semi-autonomous driving without considering the driver status.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Integrated Longitudinal Control Algorithm for Autonomous Mobility with EEG-Based Driver Status Classification and Safety Index

Jang,

2024

Electronics

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During unexpected driving situations in autonomous vehicles, such as a system failure, the driver should take over control from the vehicles in SAE Level 3 to cope with unexpected situations. Therefore, it is necessary to develop reasonable takeover technologies to ensure safe driving. In this study, an electroencephalogram (EEG)-based driver status classification model and a safety index-based integrated longitudinal control algorithm considering the takeover time and driving characteristics are proposed. The driver status is classified into two states: road monitoring and non-driving-related tasks. EEG data are acquired while the driver performs certain tasks. The driver status classification model is presented using the EEG data based on a machine learning method. It is designed such that the desired takeover time is determined based on the classified driver state. To design the integrated longitudinal control algorithm, a safety index is designed and calculated based on the vehicle state and driver’s driving characteristics. The desired clearances based on the desired takeover time and driver characteristics are calculated and selected based on the safety index. A sliding-mode control algorithm is adopted to allow the vehicle to track the desired clearance reasonably. The performance of the proposed control algorithm is evaluated using the MATLAB/Simulink R2019a (Mathworks, Natick, Massachusetts, U.S.A) and CarMaker software 8.1.1 (IPG Automotive, Karlsruhe, Germany).

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Section: Driver Status Classification Model Evaluation In Real Timementioning

confidence: 99%