Decision-Making for Autonomous Vehicles in Multi-Scenarios With Global Map Model and Dynamic Safe Topological Structure

Autonomous vehicles are expected to navigate safely and efficiently in dynamic environments, which requires the seamless integration of efficient global route planning with stable and safe local motion planning. To enhance global route planning, we construct a state-value function to represent the global map and then iterate an inheritable state-transition matrix that can be used to rapidly search globally optimal routes. To improve the safety, stability and adaptability of local motion planning, we propose an adaptive dynamic safe topological structure that combines hierarchical computational layers to decompose decision-making processes and adaptively regulate decision outputs in diverse scenarios. These methods are integrated into a unified framework for cooperative decision-making, which is validated in various scenarios in CARLA. The experimental results demonstrate that the global route planning method efficiently searches optimal routes, while the local motion planning method ensures safe and robust decisions across various driving scenarios and styles. Additionally, the integrated framework achieves effective cooperation between efficient global navigation and effective local decision-making.