Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion

TL;DR

This paper introduces a dual control framework with a model-based diffusion solver for real-time interactive autonomous merging, improving behavior inference and adaptive planning in complex traffic scenarios.

Contribution

It presents a novel active learning approach with a diffusion-based solver for online receding horizon control in autonomous driving, extending to hardware experiments.

Findings

Enhanced behavior inference in traffic scenarios

Improved adaptive planning under uncertainty

Validated in hardware experiments

Abstract

Interactive decision-making is essential in applications such as autonomous driving, where the agent must infer the behavior of nearby human drivers while planning in real-time. Traditional predict-then-act frameworks are often insufficient or inefficient because accurate inference of human behavior requires a continuous interaction rather than isolated prediction. To address this, we propose an active learning framework in which we rigorously derive predicted belief distributions. Additionally, we introduce a novel model-based diffusion solver tailored for online receding horizon control problems, demonstrated through a complex, non-convex highway merging scenario. Our approach extends previous high-fidelity dual control simulations to hardware experiments, which may be viewed at https://youtu.be/Q_JdZuopGL4, and verifies behavior inference in human-driven traffic scenarios, moving beyond idealized models. The results show improvements in adaptive planning under uncertainty, advancing the field of interactive decision-making for real-world applications.