Informed sampling-based trajectory planner for automated driving in dynamic urban environments

TL;DR

This paper presents a fast, sampling-based trajectory planner for autonomous vehicles in urban settings, integrating domain knowledge and dynamic predictions to enhance safety and efficiency.

Contribution

It introduces an improved Stable-Sparse-RRT algorithm with domain-informed exploration and iterative solutions for urban autonomous driving.

Findings

Capable of planning collision-free, comfortable trajectories in urban scenarios.

Enhanced exploration efficiency through domain-knowledge-based branches.

Simulation results demonstrate improved planning performance.

Abstract

The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of various dynamic traffic participants such as vehicles, cyclists and pedestrians and in various environmental conditions. The challenge remains to have motion planners that are computationally fast and that account for future movements of other road users proactively. This paper describes an computationally efficient sampling-based trajectory planner for safe and comfortable driving in urban environments. The planner improves the Stable-Sparse-RRT algorithm by adding initial exploration branches to the search tree based on road layout information and reiterating the previous solution. Furthermore, the trajectory planner accounts for the predicted motion of other traffic participants to allow for safe driving in urban traffic. Simulation studies show that the planner is capable of planning collision-free, comfortable trajectories in several urban traffic scenarios. Adding the domain-knowledge-based exploration branches increases the efficiency of exploration of highly interesting areas, thereby increasing the overall planning performance.