IPP: Interactive Policy Planning With Adaptive Trajectory Optimization and Joint Conditional Prediction

Autonomous vehicles need to accurately predict the multimodal behaviors of surrounding agents while planning motion policies that ensure safety, comfort, and adaptability in dynamic environments. Existing behavior prediction methods primarily model interactions based on agents’ historical trajectories but often neglect potential interactions in their future trajectories. This limitation compromises the accuracy and consistency of joint predictions. Additionally, the inherent uncertainty of dynamic environments necessitates motion strategies that can adapt to evolving scenarios. To address these challenges, this paper proposes an interactive policy planning framework that integrates adaptive trajectory optimization and joint conditional prediction modules to improve the accuracy and adaptability of motion policies in dynamic scenarios. Specifically, the adaptive trajectory optimization module incorporates a scene attribute-based trajectory refinement strategy, facilitating effective interaction between the ego vehicle’s trajectory and its surrounding environment, thereby generating accurate and adaptable trajectories. The joint conditional prediction module models future interactions among agents as a directed acyclic graph, leveraging its partial ordering structure to decompose the joint prediction task into a series of marginal and conditional predictions, thereby producing more accurate and scene-consistent predictions. Extensive experimental evaluations on the nuPlan dataset and its simulator demonstrate the superior performance of the proposed framework and modules in both trajectory prediction and closed-loop planning tasks.