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arXiv · 2502.09918

Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion

Jacob Knaup·Jovin D'sa·Behdad Chalaki·Hossein Nourkhiz Mahjoub·Ehsan Moradi-Pari·Panagiotis Tsiotras
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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.

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Jacob Knaup, Jovin D'sa, Behdad Chalaki, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari, Panagiotis Tsiotras. 2025-02-14. Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion. https://arxiv.org/abs/2502.09918

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