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Longitudinal Driving Behavior before, during, and after a Left-Turn Movement at Signalized Intersections: A Naturalistic Driving Study in China

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  • Lihong Xia

    (State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
    School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing 400067, China
    School of Automation, Chongqing University, Chongqing 400044, China)

  • Penghui Li

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Zhizhuo Su

    (State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Tao Chen

    (State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Zhaoxiang Deng

    (State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Dihua Sun

    (School of Automation, Chongqing University, Chongqing 400044, China)

Abstract

A human-like driving model can help to improve the acceptance and safety of automated driving systems (ADS). To improve the performance of human-like driving and interaction with conventional vehicles of ADS, the speed behavior of left-turn vehicles at the signalized intersection was studied using natural driving data. In this study, 374 valid data points of left-turn snippets at signalized intersections were extracted and three phases were introduced based on the reaction behavior of braking, stopping, and accelerating in the left-turn process. Firstly, a one-way ANOVA was used to study the influence of traffic density, traffic light state, intersection type, and left-turn waiting area on the reaction position of each phase and the spatial distribution of the speed. The traffic light state and traffic density were the main significant effects. Furthermore, to analyze the spatial distribution of acceleration, a method of frequency contour was conducted. The butterfly-shaped frequency contour suggested that “the closer to the stop line, the higher the variation of acceleration”. Finally, the driving parameters at each phase were further analyzed. The main results indicate the following: (1) The red traffic light will lead to a larger variation of acceleration, a larger maximum deceleration, a larger starting acceleration, and a larger maximum acceleration. (2) On the condition of dense traffic density, more stops and the duration of the stop–go phase may cause the time pressure, and the driver tends to choose a greater maximum acceleration. (3) The red traffic light leads to a further reaction distance of all three phases, whilst increased traffic density only increases the reaction distance of the stop. (4) Both the dense traffic density and red traffic light lead to an earlier reaction time. The findings can provide a basis for the design of human-like driving of left-turn driving assistance systems and improve the interaction with left-turn conventional vehicles.

Suggested Citation

  • Lihong Xia & Penghui Li & Zhizhuo Su & Tao Chen & Zhaoxiang Deng & Dihua Sun, 2022. "Longitudinal Driving Behavior before, during, and after a Left-Turn Movement at Signalized Intersections: A Naturalistic Driving Study in China," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11630-:d:916617
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    References listed on IDEAS

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    2. Vinayak V Dixit & Sai Chand & Divya J Nair, 2016. "Autonomous Vehicles: Disengagements, Accidents and Reaction Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
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