Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment

Sensors

Pan Zhao¹  Huawei Liang¹  Tao Mei¹  Jiajia Chen² 

[1] Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science,Chinese Academy of Sciences, Changzhou 213164, China;School of Engineering Science, University of Science and Technology of China,Hefei 230026, China;

关键词: autonomous vehicle;    motion planning;    radial basis function network;    gradient descent method;   

DOI  :  10.3390/s140917548

来源: DOAJ

【 摘 要 】

The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of themotion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existingwell-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.

【 授权许可】

Unknown