Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

CAV traffic accident fuel consumption prediction energy saving

Authors

  • Qingchao Liu
    lqc@ujs.edu.cn
    Automotive Engineering Research Institute, Jiangsu University; School of Mechanical and Aerospace Engineering, Nanyang Technological University; Jiangsu University Research Institute of Engineering Technology, China
  • Wenjie Ouyang Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology, China
  • Jingya Zhao Automotive Engineering Research Institute, Jiangsu University; Jiangsu University Research Institute of Engineering Technology, China
  • Yingfeng Cai Automotive Engineering Research Institute, Jiangsu University, China
  • Long Chen Automotive Engineering Research Institute, Jiangsu University, China

Downloads

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers.