A Novel Gated Recurrent Unit Network Based on SVM and Moth-Flame Optimization Algorithm for Behavior Decision-Making of Autonomous Vehicles

Abstract

The behavior decision-making algorithm plays an important role in ensuring the safe driving of autonomous vehicles. However, existing behavior decision-making methods lack the capability to cope with future motion uncertainty in traffic, because the historical state of vehicles are not considered. This article proposes a novel driving behavior decision-making method EnMFO-ImGRU based on Gated Recurrent Unit (GRU) and Moth-Flame Optimization algorithm (MFO). Four improvements are proposed in EnMFO-ImGRU. First, to consider the driving information of the vehicles on the road, ImGRU is designed based on a double-layer GRU. Second, to promote decisions accuracy, Support Vector Machine (SVM), which has good performance in classification problems, replaces the softmax classifier to train the output of the ImGRU. Third, to promote the classification capability of SVM, MFO is introduced to optimize the key parameters that affect the performance of SVM. Finally, to promote the optimization capability of MFO, we propose the Enhanced Moth-Flame Optimization algorithm (EnMFO). A new position updating method is proposed in EnMFO. The experimental results on the NGSIM dataset show that EnMFO-ImGRU brings higher accuracy than existing methods for the behavior decision-making results of autonomous vehicles.