Prediction of Automatic Parking Accuracy Based on PSO Optimized BP Neural Network

Automatic parking technology has been an indispensable part of the assisted driving systems installed in cars in recent years, and the popularity of automatic driving in society is increasing day by day. Based on the problem of low accuracy of automatic parking in complex scenes, this paper introduces the particle swarm optimization algorithm to optimize the BP neural network to improve its parking path prediction performance. By adjusting the search space of network weights and thresholds, continuous optimization, and iteration, the individual optimal value and the group optimal value are obtained, and the experiments are carried out through the fitness value and root mean square error. Compared with the traditional BP neural network and convolutional neural network, the prediction accuracy of the PSO-BP neural network is improved by about 0.04. This research result has positive significance for improving the practical application of automatic parking systems.