Intelligent Path Planning and Obstacle Avoidance Algorithms for Autonomous Vehicles Based on Enhanced RRT Algorithm

This manuscript studies the intelligent path planning and obstacle avoidance algorithms for autonomous vehicles based on enhanced RRT algorithm. The selection of the optimal path in this research gives priority to security, so the function of the weight value of each cost function is designed according to the threshold of the security cost function. When the value of the security cost function is above the threshold, the security is low. With this replacement, the traditional RRT is implemented with the genetic algorithms and the neural computation model is combined for the accuracy concern. The designed model is tested on the random data sets. Compared with the traditional RRT, the neural network based model and the fuzzy planning based model, the proposed is efficient.