An LSTM-based Game Theory Method for Multi-Agent DecisionMaking in Highway Scenarios

Decision-making plays a critical role in autonomous driving, connecting the upstream perception task and the downstream planning task. The interaction among vehicles and the uncertainty of driving intentions are two main challenges of lane change decision-making. To meet these challenges, a game theory method based on the LSTM (Long Short-Term Memeory) neural network is proposed. The game theory method is adopted to model the interaction among vehicles and the LSTM network is used to precisely fit the complex nonlinear relationship between payoffs and features. For highway scenarios with ramps, discretionary and mandatory lane change scenarios are specifically extracted and the lane priority is added to features. The improved GPSO-DE optimizer is used to accelerate network training and reduce the local optimal solutions. Finally, experiments on real-world dataset NGSIM I-80 show that the prediction accuracy of other vehicles’ intentions and the decision-making accuracy of ego vehicles have reached state-of-the-art performance. Moreover, the model is capable of improving the robustness of decision-making and reducing unreasonable jumps effectively.