Ryu, Kanghyun (University of California Berkeley), Sung, Minjun (University of Illinois Urbana Champaign), Gupta, Piyush (Honda Research Institute, US), D'sa, Jovin (Honda Research Institute, USA), Tariq, Faizan M. (Honda Research Institute USA, Inc.), Isele, David (Honda Research Institute USA), Bae, Sangjae (Honda Research Institute, USA)

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving

Scheduled for presentation during the Regular Session "S41b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (FR-EA-T41), Friday, November 21, 2025, 14:50−15:30, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV’s actions. To address this, we propose Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral (IANN-MPPI) control, which enables interactive trajectory planning by predicting how surrounding agents may react to each control sequence sampled by MPPI. To improve performance in structured lane environments, we introduce a spline-based prior for the MPPI sampling distribution, enabling efficient lane-changing behavior. We evaluate IANN-MPPI in a dense traffic merging scenario, demonstrating its ability to perform efficient merging maneuvers. Our project website is available at https://sites.google.com/berkeley.edu/iann-mppi