Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles
Hao Du, Supeng Leng, Jianhua He, Kai Xiong, Longyu Zhou
Abstract
Road obstacles that unexpectedly appear such as vehicle breakdowns and accidents, are major causes of fatal road accidents. Connected Autonomous Vehicles (CAVs) can be used to avoid collisions to ensure road safety through cooperative sensing and driving. However, the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing works. In this paper, we first design a platoon-based collision avoidance framework for CAVs. In this framework, we deploy a Digital Twin (DT) system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning scheme. In addition, an DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT system. In this case, the transmission frequency of kinetic states of platoon members to ensure low-overhead communication. Moreover, we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high reliability. To further improve road safety, an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency risks. Simulation results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle avoidance. Compared to the existing schemes, it can reduce collisions by 95% and is faster by about 10% passing by the unexpected obstacle.