Optimal Vehicle Path Planning Using Quadratic Optimization for Baidu Apollo Open Platform

TL;DR

This paper introduces a quadratic programming method for autonomous vehicle path planning that ensures collision avoidance and smooth, feasible trajectories in complex urban environments.

Contribution

It proposes a novel quadratic optimization approach that guarantees collision-free, kinematically feasible paths for autonomous vehicles in cluttered settings.

Findings

Achieves collision avoidance in complex scenarios

Generates smooth, feasible vehicle trajectories

Demonstrates effectiveness in urban driving environments

Abstract

Path planning is a key component in motion planning for autonomous vehicles. A path specifies the geometrical shape that the vehicle will travel, thus, it is critical to safe and comfortable vehicle motions. For urban driving scenarios, autonomous vehicles need the ability to navigate in cluttered environment, e.g., roads partially blocked by a number of vehicles/obstacles on the sides. How to generate a kinematically feasible and smooth path, that can avoid collision in complex environment, makes path planning a challenging problem. In this paper, we present a novel quadratic programming approach that generates optimal paths with resolution-complete collision avoidance capability.