SIF-STGDAN: A Social Interaction Force Spatial-Temporal Graph Dynamic Attention Network for Decision-Making of Connected and Autonomous Vehicles
2024 IEEE Intelligent Vehicles Symposium (IV)(2024)
Abstract
The collaborative decision-making technology of connected and autonomous vehicles (CAVs) is critical in today’s autonomous driving. Recently, graph reinforcement learning (GRL)-based methods have demonstrated exemplary performance in solving decision-making problems by implementing graphic technologies. However, current GRL-based research faces the challenge of modeling the interaction completely and extracting driving features efficiently. To address these issues, this paper proposes a social interaction force (SIF) spatial-temporal graph dynamic attention network (SIF-STGDAN) to solve the decision-making of CAVs. First, a SIF model is established to better represent the mutual effect between vehicles; an on-ramp merging scenario is then constructed and modeled by graph representation. Then, the SIF-STGDAN is proposed by combining the temporal convolutional network (TCN) and graph dynamic attention network to extract the graphic features of the on-ramp scenario efficiently, and the double deep q-learning (DDQN) algorithm is utilized to generate the optimized driving strategies for CAVs. Finally, experiments are conducted, and results show that our proposed SIF-STGDAN outperforms the baselines in terms of safety, efficiency, and model stability.
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Key words
Dynamic Network,Autonomous Vehicles,Social Forces,Graphical Representation,Mutual Effect,Decision-making Problems,Graph Features,Collaborative Decision-making,Temporal Convolutional Network,Double Deep Q-network,Deep Learning,Learning Rate,Undirected,Attention Mechanism,Average Speed,Reward Function,Deep Reinforcement Learning,Markov Decision Process,Graph Convolutional Network,Decision-making Model,Deep Reinforcement Learning Model,Graph Neural Networks,Deep Q-network,Graph Attention Network,Proximal Policy Optimization,Rule-based Methods,Policy Network,Decision-making System,Traffic Scenarios,Attention Weights
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