Real-time Motion Planning for autonomous vehicles in dynamic environments

TL;DR

This paper presents a robust, hierarchical motion planning algorithm for autonomous vehicles that effectively navigates dynamic environments with moving obstacles, improving safety and efficiency.

Contribution

It introduces a novel path density adjustment method and integrates global and local planning algorithms for better real-time trajectory planning.

Findings

Effective in diverse simulation scenarios

Reduces computational complexity in straight paths

Enhances safety and navigation efficiency

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.