Integrated path planning and control for autonomous vehicle platooning

Platooning provides various benefits, such as improving traffic system efficiency and reducing fuel consumption. Existing research on platooning has primarily concentrated on longitudinal control in highways or dedicated road environments. However, these approaches can significantly reduce platooning performance during scenarios requiring lateral maneuvers, such as cornering or obstacle avoidance. To address these limitations, this study proposes a comprehensive platooning system that considers both longitudinal and lateral dynamics. The proposed platooning approach comprises model predictive control (MPC)-based path planning incorporating the constant time gap (CTG) strategy, and integral control-based path tracking. The MPC-based path planning is formulated as an optimal control problem aimed at minimizing the total cost, which includes the candidate path cost based on CTG policy-generated velocity commands and environmental costs. The optimal control input is obtained using particle swarm optimization (PSO), resulting in the generation of an optimal path. For path tracking, an integral error for yaw rate is defined, and an integral control-based method is employed, considering the differential equation of the integral error and the vehicle’s dynamics model. Numerical simulations and indoor experiments are conducted to validate the feasibility of the proposed approach, with an analysis of the results. A validation video is available at: https://youtu.be/-F4xney2Yjc.