Comparison of Path Planning Approaches of Autonomous Vehicles for Obstacle Avoidance Application

Abstract

This report investigates path planning and trajectory generation algorithms for the application in autonomous vehicles for obstacle avoidance. A literature review is conducted to select path planning and trajectory generation algorithms that are suitable for the obstacle avoidance application. Two path planning approaches are designed in this work. Approach 1 (RRT*-Spline) uses rapidly exploring random trees* (RRT*) path planning algorithm combined with cubic spline trajectory generation algorithm. Approach 2 (A*-Polynomial Curve) plans a feasible path by using A* algorithm and generates a smooth trajectory using 5th order polynomial curve fitting algorithm. To demonstrate obstacle avoidance of autonomous vehicles, Prescan and Matlab/Simulink are used to build an integrated model consisting of path planning and trajectory generation algorithms, an ego vehicle model, a Model Predictive Control (MPC) controller to control the longitudinal and lateral motion of the ego vehicle, and obstacle avoidance scenarios. Simulation tests are performed to validate the developed algorithms and compare the performances of two approaches for both stationary and moving obstacles. A summary of the performance comparison is provided with respect to the smoothness of the reference path, the smoothness of the reference trajectory, and the ego vehicle steering angle change while it performs an obstacle avoidance task.