Automotive Innovation ›› 2023, Vol. 6 ›› Issue (1): 32-47.doi: 10.1007/s42154-022-00209-w
Towards Human-Vehicle Interaction: Driving Risk Analysis Under Different Driver Vigilance States and Driving Risk Detection Method
Yingzhang Wu1 · Jie Zhang2 · Wenbo Li3 · Yujing Liu1 · Chengmou Li1 · Bangbei Tang4 · Gang Guo1
- 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
2. Chongqing Changan Automobile Corporation Ltd., Chongqing 401133, China
3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
4. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China
Towards Human-Vehicle Interaction: Driving Risk Analysis Under Different Driver Vigilance States and Driving Risk Detection Method
Yingzhang Wu1 · Jie Zhang2 · Wenbo Li3 · Yujing Liu1 · Chengmou Li1 · Bangbei Tang4 · Gang Guo1
- 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
2. Chongqing Changan Automobile Corporation Ltd., Chongqing 401133, China
3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
4. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China
摘要： The driver's behavior plays a crucial role in transportation safety. It is widely acknowledged that driver vigilance is a major contributor to traffic accidents. However, the quantitative impact of driver vigilance on driving risk has yet to be fully explored. This study aims to investigate the relationship between driver vigilance and driving risk, using data recorded from 28 drivers who maintain a speed of 80 km/h on a monotonous highway for 2 hours. The k-means and linear fitting methods are used to analyze the driving risk distribution under different driver vigilance states. Additionally, this study proposes a research framework for analyzing driving risk and develops three classification models (KNN, SVM, and DNN) to recognize the driving risk status. The results show that the frequency of low-risk incidents is negatively correlated with the driver's vigilance level, whereas the frequency of moderate-risk and high-risk incidents is positively correlated with the driver's vigilance level. The DNN model performs the best, achieving an accuracy of 0.972, recall of 0.972, precision of 0.973, and f1-score of 0.972, compared to KNN and SVM. This research could serve as a valuable reference for the design of warning systems and intelligent vehicles.