Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles

WU Bing1 WANG Wen-xuan1 LI Lin-bo1 LIU Yan-ting1

(1.Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, China 201804)

【Abstract】In order to better simulate the car-following characteristics of connected autonomous vehicles (CAV), based on the longitudinal control model (LCM), the LCM in the connected autonomous environment (C-LCM) was constructed when the influences of speed and acceleration of multiple preceding vehicles in V2V environment were considered. The stabilities of LCM and C-LCM were analyzed. The stability regions of two models were compared, and the influence of C-LCM on the traffic flow stability region at different communication distances was determined. Numerical simulation was designed to simulate the common traffic scenarios including acceleration and deceleration, and the car-following behavior characteristics of CAV in V2V environment were analyzed. The traffic flow safety levels at different communication distances and penetration rates of CAV were analyzed via simulation. A fundamental diagram model of mixed traffic flows at different penetration rates of CAV was constructed. Analysis result shows that the traffic flow stability region increases as the number of preceding vehicles rises. When only one preceding vehicle is considered, longer distance between the preceding vehicle and ego vehicle results in higher influence of velocity coefficient on the C-LCM stability region. The C-LCM can respond to the behaviors of multiple preceding vehicles in advance and simulate the dynamics characteristics of connected autonomous vehicles better. In the deceleration scenario, the speed overshoot decreases from 0.15 to 0.08, and the maximum speed delay decreases from 7.5 s to 4.9 s. In the acceleration scenario, the speed overshoot decreases from 0.07 to 0.04 and the minimum speed delay decreases from 3.5 s to 2.6 s. With the increase in CAV penetration rate, the safety level of traffic flow is enhanced. The highest safety level is achieved with four CAVs in communication distance, and TIT and TETdrop by 57.22% and 59.08%, respectively. As the CAV penetration rate goes up, the traffic capacity rises from 1 281 veh·h−1 to 3 204 veh·h−1. So the proposed C-LCM can describe the car-following characteristics of different vehicles to achieve the modeling of mixed traffic flow, decrease the complexity of mixed traffic flow, and provide a reference for the impact analysis of CAV on traffic flow.

【Keywords】 traffic safety; connected autonomous vehicles; numerical simulation; car-following model; traffic flow stability;

【DOI】

【Funds】 National Key Research and Development Project of China (2019YFB1600703)

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This Article

ISSN:1671-1637

CN: 61-1369/U

Vol 20, No. 02, Pages 184-194

April 2020

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Abstract

  • 0 Introduction
  • 1 Car-following model of connected autonomous vehicles
  • 2 Linear stability analysis
  • 3 Simulation validation
  • 4 Impact of safety level evaluation
  • 5 Impact analysis of fundamental diagram
  • 6 Conclusions
  • References