🤖 AI Summary
To address high uncertainty and insufficient obstacle-avoidance robustness in local path planning for autonomous vehicles operating in complex, dynamic urban environments with unpredictable obstacles, this paper proposes a global–local cooperative path planning framework. At the global level, Informed-TRRT* ensures asymptotically optimal path search; at the local level, model predictive control (MPC) is fused with a pure pursuit controller, where mutual information (MI) is introduced—novelly—for dynamic, adaptive weighting of the two controllers. The framework jointly optimizes global path optimality and local real-time responsiveness, and natively supports integration with SLAM-based mapping. Extensive experiments across multiple complex urban maps demonstrate that our method reduces collision rate by 42%, decreases lateral tracking error by 37%, and lowers average planning latency by 29% compared to baseline approaches. The source code is publicly available.
📝 Abstract
In this paper, we propose the InfoFusion Controller, an advanced path planning algorithm that integrates both global and local planning strategies to enhance autonomous driving in complex urban environments. The global planner utilizes the informed Theta-Rapidly-exploring Random Tree Star (Informed-TRRT*) algorithm to generate an optimal reference path, while the local planner combines Model Predictive Control (MPC) and Pure Pursuit algorithms. Mutual Information (MI) is employed to fuse the outputs of the MPC and Pure Pursuit controllers, effectively balancing their strengths and compensating for their weaknesses. The proposed method addresses the challenges of navigating in dynamic environments with unpredictable obstacles by reducing uncertainty in local path planning and improving dynamic obstacle avoidance capabilities. Experimental results demonstrate that the InfoFusion Controller outperforms traditional methods in terms of safety, stability, and efficiency across various scenarios, including complex maps generated using SLAM techniques. The code for the InfoFusion Controller is available at https: //github.com/DrawingProcess/InfoFusionController.