DDM-Lag : A Diffusion-based Decision-making Model for Autonomous Vehicles with Lagrangian Safety Enhancement

Decision-making stands as a pivotal component in the realm of autonomous vehicles (AVs), playing a crucial role in navigating the intricacies of autonomous driving. Amidst the evolving landscape of data-driven methodologies, enhancing decision-making performance in complex scenarios has emerged as a prominent research focus. Despite considerable advancements, current learning-based decision-making approaches exhibit potential for refinement, particularly in aspects of policy articulation and safety assurance. To address these challenges, we introduce DDM-Lag, a Diffusion Decision Model,augmented with Lagrangian-based safety enhancements.In our approach, the autonomous driving decision-making conundrum is conceptualized as a Constrained Markov Decision Process (CMDP). We have crafted an Actor-Critic framework, wherein the diffusion model is employed as the actor,facilitating policy exploration and learning. The integration of safety constraints in the CMDP and the adoption of a Lagrangian relaxation-based policy optimization technique ensure enhanced decision safety. A PID controller is employed for the stable updating of model parameters. The effectiveness of DDM-Lag is evaluated through different driving tasks, showcasing improvements in decision-making safety and overall performance compared to baselines.

Measuring Sociality in Driving Interaction

Interacting with other human road users is one of the most challenging tasks for autonomous vehicles. To generate congruent driving behaviors, the awareness and understanding of sociality, which includes implicit social customs and individualized social preferences of human drivers, are required. To understand and quantify the complex sociality in driving interactions, we propose a Virtual-Game-based Interaction Model (VGIM) that is explicitly parameterized by a social preference measurement, Interaction Preference Value (IPV), which is designed to capture the driver's relative preference for individual rewards over group rewards. A method for identifying IPV from observed driving trajectory is also provided. Then, we analyze human drivers' IPV with driving data recorded in a typical interactive driving scenario, the unprotected left turn. The results show that (1) human drivers express varied social preferences in executing different tasks (turning left or going straight); (2) competitive actions are strategically conducted by human drivers in order to coordinate with others. Finally, we implement the humanlike IPV expressing strategy with a rule-based method and embed it into VGIM and optimization-based motion planners. Controlled simulation experiments are conducted, and the results demonstrate that (1) IPV identification could improve the motion prediction performance in interactive driving scenarios and (2) dynamic IPV expressing strategy extracted from human driving data makes it possible to reproduce humanlike coordination patterns in the driving interaction.