Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear Programming Approach

TL;DR

This paper introduces a linear programming approach for velocity planning in autonomous vehicles that considers jerk limits and obstacle avoidance, ensuring safety, compliance, and comfort efficiently.

Contribution

It presents a novel LP-based velocity planning method incorporating jerk constraints and obstacle avoidance, outperforming existing optimization techniques in efficiency.

Findings

The proposed method generates velocity profiles satisfying safety, comfort, and traffic rules.

It outperforms other optimization-based approaches in computational efficiency.

Validated on a real vehicle, demonstrating practical effectiveness.

Abstract

Velocity Planning for self-driving vehicles in a complex environment is one of the most challenging tasks. It must satisfy the following three requirements: safety with regards to collisions; respect of the maximum velocity limits defined by the traffic rules; comfort of the passengers. In order to achieve these goals, the jerk and dynamic objects should be considered, however, it makes the problem as complex as a non-convex optimization problem. In this paper, we propose a linear programming (LP) based velocity planning method with jerk limit and obstacle avoidance constraints for an autonomous driving system. To confirm the efficiency of the proposed method, a comparison is made with several optimization-based approaches, and we show that our method can generate a velocity profile which satisfies the aforementioned requirements more efficiently than the compared methods. In addition, we tested our algorithm on a real vehicle at a test field to validate the effectiveness of the proposed method.