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Autonomous vehicles need to accomplish their tasks while interacting withhuman drivers in traffic. It is thus crucial to equip autonomous vehicles withartificial reasoning to better comprehend the intentions of the surroundingtraffic, thereby facilitating the accomplishments of the tasks. In this work,we propose a behavioral model that encodes drivers' interacting intentions intolatent social-psychological parameters. Leveraging a Bayesian filter, wedevelop a receding-horizon optimization-based controller for autonomous vehicledecision-making which accounts for the uncertainties in the interactingdrivers' intentions. For online deployment, we design a neural networkarchitecture based on the attention mechanism which imitates the behavioralmodel with online estimated parameter priors. We also propose a decision treesearch algorithm to solve the decision-making problem online. The proposedbehavioral model is then evaluated in terms of its capabilities for real-worldtrajectory prediction. We further conduct extensive evaluations of the proposeddecision-making module, in forced highway merging scenarios, using bothsimulated environments and real-world traffic datasets. The results demonstratethat our algorithms can complete the forced merging tasks in various trafficconditions while ensuring driving safety.

Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network