让自动驾驶车实时推断他人行为并动态调整策略
Dual Control for Interactive Autonomous Merging with Model Predictive Diffusion
- 用扩散模型实现在线滚动时域控制，动态优化决策
- 在复杂高速变道场景中提升不确定环境下的规划适应性
- 首次在真实硬件上验证交互式决策，适合智能驾驶研究者
交互式决策在自动驾驶等应用中至关重要，要求智能体实时推断周围人类驾驶员的行为并进行规划。传统预测-执行框架常因缺乏持续交互而效果不佳。为此，我们提出一种主动学习框架，严格推导预测信念分布，并引入一种新型基于模型的扩散求解器，专用于在线滚动时域控制问题。该方法在复杂的非凸高速公路变道场景中得到验证，首次将高保真双控制仿真扩展至硬件实验（视频可见：https://youtu.be/Q_JdZuopGL4），成功在人类驾驶交通场景中验证行为推断能力，突破理想化模型限制。结果表明，在不确定性环境下，该方法显著提升了自适应规划性能，推动了交互式决策在真实场景中的应用进展。
原文摘要 · Abstract (English)
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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