Situation-Aware Interactive MPC Switching for Autonomous Driving

TL;DR

This paper proposes a situation-aware switching strategy for different MPC controllers in autonomous driving, balancing interaction fidelity and computational efficiency by using a neural network classifier to select the appropriate controller based on traffic context.

Contribution

It introduces a neural network-based classifier for dynamic switching among MPC formulations, optimizing performance and computational load in autonomous driving scenarios.

Findings

Situation-aware switching improves critical response performance.

Computational load is significantly reduced in non-critical scenarios.

Hierarchized MPC formulations enable adaptive interaction modeling.

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.