Situation-Aware Interactive MPC Switching for Autonomous Driving

2025/12/05 by Qi, Shuhao, Aori, Qiling, Zhang, Luyao +2
#FOS: Computer and information sciences #FOS: Electrical engineering #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2512.06182

Abstract

To enable autonomous driving in interactive traffic scenarios, various model predictive control (MPC) formulations have been proposed, each employing different interaction models. While higher-fidelity models enable more intelligent behavior, they incur increased computational cost. Since strong interactions are relatively infrequent in traffic, a practical strategy for balancing performance and computational overhead is to invoke an appropriate controller based on situational demands. To achieve this approach, we first conduct a comparative study to assess and hierarchize the interactive capabilities of different MPC formulations. Furthermore, we develop a neural network-based classifier to enable situation-aware switching among controllers with different levels of interactive capability. We demonstrate that this situation-aware switching can both substantially improve overall performance by activating the most advanced interactive MPC in rare but critical situations, and significantly reduce computational load by using a basic MPC in the majority of scenarios.

Citations

• Online Learning of Interaction Dynamics with Dual Model Predictive Control for Multi-Agent Systems Using Gaussian Processes
• Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging
• Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach
• Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control
• Interactive multi-modal motion planning with Branch Model Predictive Control
• Anytime Game-Theoretic Planning with Active Reasoning About Humans' Latent States for Human-Centered Robots
• An Approximate Dynamic Programming Approach for Dual Stochastic Model Predictive Control
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

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• Interaction-aware Model Predictive Control for Autonomous Driving
• Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network