Construction of Traffic Control Scenarios for Energy-Saving Trucks Driving

• Main Authors: Sheng, Feng, Zeng, Xiaoqing, Deng, Bo, An, Xuehui, Yue, Xiaoyuan, Wang, Yizeng
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptation models; Animals; Biological system modeling; connected trucks; Energy consumption; Reinforcement learning; Traffic control; traffic control scenarios; Velocity control
• ISSN: 2643-2978
• DOI: 10.1109/ICIT58233.2024.10541031

Frequent traffic safety accidents on ecological roads have become the focus of public attention. Given the adaptability of animal movements, conventional warning signs prove insufficient in maintaining a smooth flow of traffic on the road. Therefore, there is an urgent need for a new traffic control model that can effectively solve animal-vehicle collision accidents. In response to the frequent traffic accidents between animals and vehicles in recent years, this paper proposes a strategy for constructing traffic control scenarios for energy-saving truck driving. The strategy includes detecting animal crossing information in advance, and based on the internet of vehicle communication technology, the information is received by connected trucks on the road. By processing animal crossing behavior into virtual signal control, a dual-objective speed optimization control model is proposed. Simulation experiments based on reinforcement learning algorithms show that the construction of this control scenario can effectively reduce the risk of animal-vehicle collisions and reduce energy consumption. This research has practical reference value for energy-saving driving and speed control in the Internet of Vehicles environment.