Optimal Vehicle Trajectory Planning for Static Obstacle Avoidance using Nonlinear Optimization

TL;DR

This paper introduces a nonlinear optimization-based vehicle trajectory planning algorithm that efficiently computes comfortable, collision-free paths with static obstacles in real-time, suitable for autonomous driving systems.

Contribution

The paper presents a novel nonlinear optimization method that ensures kinematic feasibility and comfort while achieving rapid computation suitable for real-time deployment.

Findings

Generates a 6-second trajectory within 10-20 milliseconds.

Ensures collision avoidance with static obstacles.

Achieves kinematic feasibility and comfort optimization.

Abstract

Vehicle trajectory planning is a key component for an autonomous driving system. A practical system not only requires the component to compute a feasible trajectory, but also a comfortable one given certain comfort metrics. Nevertheless, computation efficiency is critical for the system to be deployed as a commercial product. In this paper, we present a novel trajectory planning algorithm based on nonlinear optimization. The algorithm computes a kinematically feasible and comfort-optimal trajectory that achieves collision avoidance with static obstacles. Furthermore, the algorithm is time efficient. It generates an 6-second trajectory within 10 milliseconds on an Intel i7 machine or 20 milliseconds on an Nvidia Drive Orin platform.