Comparison of Path Planning Approaches of Autonomous Vehicles for Obstacle Avoidance Application
2022-01-0090
03/29/2022
- Content
- This paper investigates different 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 motion 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 is used to build real life driving scenarios which include an ego vehicle, an obstacle, sensors, roads, and other necessary components. MATLAB script and Simulink are used to build an integrated model consisting of path planning and trajectory generation algorithms, and a Model Predictive Control (MPC) controller to control the longitudinal and lateral motions of the ego vehicle. Simulation tests are performed to validate the developed algorithms and compare the performance of two motion planning approaches for two different scenarios. The first scenario has a stationary obstacle which blocks the path of the ego vehicle. The second scenario has a moving obstacle which drives in front of the ego vehicle. The performance comparison is presented in the paper 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 maneuver.
- Citation
- Zhang, D., and Chen, B., "Comparison of Path Planning Approaches of Autonomous Vehicles for Obstacle Avoidance Application ," SAE Technical Paper 2022-01-0090, 2022, .