An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

TL;DR

This paper introduces a real-time spatial-temporal trajectory planning method for autonomous vehicles that efficiently handles unstructured environments and dynamic obstacles by leveraging differential flatness and safe driving corridors.

Contribution

The proposed method simplifies trajectory optimization using differential flatness and introduces a safe corridor approach for obstacle avoidance in unstructured environments.

Findings

Outperforms state-of-the-art methods in efficiency and trajectory quality.

Demonstrated real-world applicability through experiments.

Achieves real-time planning with complex environmental constraints.

Abstract

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community