DP and QP Based Decision-making and Planning for Autonomous Vehicle

TL;DR

This paper presents a decision-making and planning framework for autonomous vehicles using dynamic programming for global path planning and quadratic programming for local trajectory optimization, tested through simulations.

Contribution

It introduces a novel integrated approach combining DP and QP with S-T graphs for obstacle avoidance in autonomous driving.

Findings

Effective in static and dynamic obstacle avoidance

Robust performance in complex scenarios

Feasible for real-world autonomous driving

Abstract

Autonomous driving technology is rapidly evolving and becoming a pivotal element of modern automation systems. Effective decision-making and planning are essential to ensuring autonomous vehicles operate safely and efficiently in complex environments. This paper introduces a decision-making and planning framework for autonomous vehicles, leveraging dynamic programming (DP) for global path planning and quadratic programming (QP) for local trajectory optimization. The proposed approach utilizes S-T graphs to achieve both dynamic and static obstacle avoidance. A comprehensive vehicle dynamics model supports the control system, enabling precise path tracking and obstacle handling. Simulation studies are conducted to evaluate the system's performance in a variety of scenarios, including global path planning, static obstacle avoidance, and dynamic obstacle avoidance involving pedestrian interactions. The results confirm the effectiveness and robustness of the proposed decision-making and planning algorithms in navigating complex environments, demonstrating the feasibility of this approach for autonomous driving applications.