Intelligent vehicle decision-making strategy integrating spatiotemporal features at roundabout

Abstract

To overcome the problems of weak driving safety and low traffic efficiency of intelligent vehicles in roundabout scenarios, and to improve the autonomous decision-making ability of intelligent systems. In this paper, we propose an intelligent vehicle Decision-Making Strategy based on SpatioTemporal graph neural Networks, namely DMS-STNet. Using end-to-end deep learning methods based on the historical information of intelligent vehicles and surrounding vehicles, output the action sequence of future driving behavior of intelligent vehicles. Specifically, a spatiotemporal graph is used to model the driving environment of vehicles, and a graph convolutional neural network is used to explore the spatial interaction relationship between intelligent vehicles and environmental vehicles. Next, based on the time convolutional network, learn the temporal characteristics of intelligent vehicles. Further integrate the complex spatiotemporal interaction relationship between intelligent vehicles and environmental vehicles through a gated fusion network. Moreover, a multi-layer perceptron is used to map the fused tensor into a sequence of driving behavior actions. In addition, experimental data collection and software in the loop testing verification were conducted on the Carla simulator platform. The research results indicate that the model proposed in this paper outperforms the comparative models in terms of prediction accuracy, safety, and traffic efficiency, fully leveraging the autonomous decision-making performance advantages of intelligent vehicles.