Efficient and Energy-Saving Cooperative Motion Planning for Multiple Connected and Autonomous Vehicles at Unsignalized Intersections

Unsignalized intersections represent a typical societal road scenario, where severe spatio-temporal conflicts occur in the central area. The indiscriminate competition for spatio-temporal resources by vehicles at intersections leads to low traffic efficiency and frequent accidents. This paper proposes a universal and unified multi-vehicle cooperative motion planning framework for intersections, coupling optimization scheduling with vehicle motion control tasks, with the aim of achieving more rational resource allocation and enhanced efficiency. Specifically, the proposed optimal control conflict-based search (OPC-CBS) algorithm constructs a conflict search tree for spatio-temporal conflict detection, and further formulates a multi-objective optimization control problem based on conflict objects. This effectively transforms the large-scale global optimization problem into a small-scale multi-stage optimization problem, achieving a balance between optimality and computational efficiency. The algorithm efficiently establishes the passing order and motion trajectories of connected and automated vehicles (CAVs) in continuous spatio-temporal domains. Simulation experiments demonstrate that the proposed algorithm can comprehensively address multiple objectives such as vehicle kinematic constraints, environmental constraints, and performance constraints in complex and dynamic scenarios. It achieves approximately an 89.06% improvement in solution efficiency while reducing energy consumption by around 17.11%.