Development of Autonomous Vehicle Motion Planning and Control Algorithms with D* Planner and Model Predictive Control

Document Type

Conference Proceeding

Publication Date

2-14-2024

Department

Department of Mechanical Engineering-Engineering Mechanics

Abstract

This paper presents the development of motion planning and control algorithms for autonomous vehicles in a dynamic environment. Path planning are implemented using the D Star (Dynamic A Star) path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm are tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Test scenario is built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle (ego vehicle) follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions.

Publication Title

Lecture Notes in Networks and Systems

ISBN

9783031477171

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