🤖 AI Summary
To address the challenge of real-time motion planning for autonomous vehicles in dynamic environments with multiple classes of moving obstacles, this paper proposes a hierarchical motion planning framework integrating global and local planning. Methodologically: (1) a path-density-adaptive sampling strategy is designed to improve coverage efficiency in the path space; (2) an enhanced A* algorithm enables rapid global path generation; and (3) a coupled approach integrates multi-motion-model-based obstacle prediction with time-varying elastic band (TEB) local optimization to ensure robust responsiveness to dynamic obstacles. Simulation results demonstrate that the proposed method significantly improves trajectory smoothness, computational real-time performance, and collision-avoidance safety under complex dynamic scenarios. Furthermore, the framework exhibits cross-platform compatibility, supporting deployment across diverse autonomous vehicles and mobile robots.
📝 Abstract
Recent advancements in self-driving car technologies have enabled them to navigate autonomously through various environments. However, one of the critical challenges in autonomous vehicle operation is trajectory planning, especially in dynamic environments with moving obstacles. This research aims to tackle this challenge by proposing a robust algorithm tailored for autonomous cars operating in dynamic environments with moving obstacles. The algorithm introduces two main innovations. Firstly, it defines path density by adjusting the number of waypoints along the trajectory, optimizing their distribution for accuracy in curved areas and reducing computational complexity in straight sections. Secondly, it integrates hierarchical motion planning algorithms, combining global planning with an enhanced $A^*$ graph-based method and local planning using the time elastic band algorithm with moving obstacle detection considering different motion models. The proposed algorithm is adaptable for different vehicle types and mobile robots, making it versatile for real-world applications. Simulation results demonstrate its effectiveness across various conditions, promising safer and more efficient navigation for autonomous vehicles in dynamic environments. These modifications significantly improve trajectory planning capabilities, addressing a crucial aspect of autonomous vehicle technology.