LEAD: Learning-Enhanced Adaptive Decision-Making for Autonomous Driving in Dynamic Environments

This paper proposes a Learning-Enhanced Adaptive Decision-Making (LEAD) framework for autonomous vehicles (AVs) focusing on dynamic merging scenarios. To capture the competitive and strategic nature of vehicle interactions, we develop an interaction behavior model based on non-cooperative game theory. The behavior is modeled as a dynamic game, where each vehicle optimizes its actions using a multifactorial reward function. To optimize the behavior model parameters, maximum entropy inverse reinforcement learning (IRL) is employed to acquire optimal matching parameters. Additionally, a behavioral decision-making framework LEAD adapted to dynamic environments is proposed. By establishing a mapping between environmental variables and behavior model parameters, it enables parameters online learning and recognition, and achieves interactive behavior probabilities of AVs. Quantitative analysis employing naturalistic driving datasets (highD and exiD) and real-vehicle test data validates LEAD’s high consistency with human decision-making. In 188 tested interaction scenarios, the average human-like similarity rate is 81.73%, with a notable 83.12% in the highD dataset. Furthermore, in 145 dynamic interactions, LEAD matches human decisions at 77.12%, with 6913 consistence instances. Moreover, in real-vehicle tests, a 72.73% similarity with 0% safety violations is obtained. Results demonstrate the effectiveness of our LEAD framework in enabling AVs to make informed, adaptive behavior decisions in interactive environments.