Trajectory planning for autonomous modular vehicle docking and autonomous vehicle platooning operations

• Propose a two-stage platooning trajectory planning problem. • Analyze theoretical properties and construct solution feasible regions. • Analytically solve the first-stage problem. • Develop a fast heuristic to solve the second-stage problem to the near optima. • Verify the heuristic performance with extensive experiments. Emerging autonomous modular vehicle (AMV) technology allows vehicle units to physically dock on or split from each other en route to form vehicles of different lengths. This technology has great potential in roadway logistics where platoons/long trains are formed to transport goods and passengers, i.e., freight and transit systems. AMV docking is an extreme case of autonomous vehicle (AV) platooning in that AMVs are physically connected with zero gaps. This paper formulates the AMV docking and AV platooning trajectory planning problem into a two-stage optimization problem. A feasible cone method is proposed to reveal the theoretical properties of solution feasibility and solve the first-stage problem analytically. This method provides the basics for a parsimonious heuristic approach to design trajectories specified as several quadratic segments. A heuristic alternative solution based on Pontryagin's maximum principle is proposed to solve a special case of the original problem to the exact optimum. Then an exact solution approach based on quadratic programming is proposed to optimize the trajectories. The feasible cone method is used to construct valid cuts to expedite the exact solution efficiency. Numerical experiments show that the parsimonious heuristic approach can achieve near-optimal solutions and greatly reduce the solution time compared with the exact solution approach, appealing to real-time engineering applications. The results also demonstrate the superiority of the parsimonious heuristic approach in optimizing AMV docking and AV platooning trajectories compared with traditional platooning methods. Sensitivity analysis results shed insights into advising parameter selections of platoon-related logistics to balance the tradeoff between operational efficiency and cost.