Connected Automated Vehicles Path Planning and Formation Control Considering Vehicle Lateral and Longitudinal Dynamics

This paper investigates the path planning and formation control problem for connected automated vehicles (CAVs) in unstructured road scenarios. A hierarchical framework that integrates path planning and vehicle dynamics control is proposed for multiple CAVs to form specific formations without lane information of the road. In the path planning layer, a virtual leader is used to guide the position and direction of the formation and the decentralized path planning algorithm for multiple CAVs is developed based on the virtual leader's information through the communication network. A spatial and temporal graph (STG) is constructed based on the virtual leader's local path and the specific formation shape. The improved A* method based on the STG is proposed to generate the coarse path for the following vehicles. Then the path is smoothed by the B-spline method. In the dynamics control layer, the model predictive control (MPC) and linear quadratic regulator (LQR) are used to track the desired point based on the vehicle lateral and longitudinal dynamics model. Simulation examples validate the efficiency of our framework.