Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles

Tong Liu, Chang Wang (), Rui Fu, Yong Ma, Zhuofan Liu and Tangzhi Liu
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Tong Liu: College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Chang Wang: Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China
Rui Fu: Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China
Yong Ma: Key Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Transport, Chang’an University, Xi’an 710064, China
Zhuofan Liu: Modern Postal School, Xi’an University of Posts & Telecommunications, Xi’an 710061, China
Tangzhi Liu: College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Sustainability, 2022, vol. 14, issue 16, 1-20

Abstract: The research of early warning and control strategy considering driving styles during lane changes is a hotspot in the field of automatic driving. However, many lane-changing studies only emphasize the warning analysis when the following vehicle is faster than the subject vehicle, while neglecting the potential risk when the subject vehicle is faster than the following vehicle in the adjacent lane during lane changes. To study the lane-changing characteristics of drivers considering driving styles and to establish a personalized lane-changing warning model under different relative speed conditions, fifty participants (three females and forty-seven males) were recruited to carry out a real road driving test. A novel Gaussian mixture model with the results of k-means clustering was established to classify driving styles based on two-dimensional variables: average time gap and average minimum time to collision. The clustering result was then verified. In addition, by analyzing the relationship between the subject vehicle and the following vehicle in the adjacent lane during lane changes, a lane-changing warning model considering driving styles under different relative speed conditions was established. Results show that the clustering algorithm proposed in this paper has high separability between samples, achieving a much softer clustering result that can provide a reference for the parameter setting of the personalized driver assistance system. Furthermore, the overall recognition accuracy of the hazardous lane-changing behaviors improved after drivers were classified into different driving styles. The established lane-changing warning model has a better recognition performance for aggressive drivers when compared with the other two driver types. The results provide a basis for the algorithm design of the intelligent lane-changing warning system and can improve the user acceptance of an advanced driver assistance system for self-driving vehicles.

Keywords: lane-changing warning model; relative speed ranges; cluster analysis; Gaussian mixture model; driving style (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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