让自动驾驶车与机器人在复杂路口安全共行，避免碰撞。
Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections
- 用可微分预测模型实时评估动作风险，精准修正行为
- 高密度混合交通中碰撞率低于5.6%，性能领先
- 适合需要高安全性的城市智能交通系统研发
自动驾驶车辆与移动机器人在城市环境中融合应用，对智能交通系统的安全性提出严峻挑战。本文针对动态特性各异的异构智能体在无监管路口的协同问题，提出一种可微分模型预测安全框架（DMPS）。该框架将模型预测控制的前瞻能力嵌入数据驱动的端到端强化学习架构中，使智能体通过学习隐式动力学模型来预测自身动作下的未来轨迹，并由一个可微分的安全评价器评估轨迹风险。关键在于，利用反向传播遍历整个预测过程，智能体可高效计算出未来安全度对当前动作的梯度，从而实现最小且精确的在线安全修正。在多智能体训练框架下，该方法在高密度混合车-机交通仿真中几乎消除碰撞，碰撞率低于5.6%，在保障能量与通行效率的同时达到业界领先的安全水平。
原文摘要 · Abstract (English)
The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transportation systems. This paper addresses the complex problem of coordinating heterogeneous agents with disparate dynamics at unregulated intersections. We introduce a novel framework, differentiable model predictive safety (DMPS), which embeds the foresight of model-predictive control into a data-driven, end-to-end reinforcement learning architecture. DMPS agents learn a latent dynamics model to predict future trajectories contingent on their actions. A learned, differentiable safety critic then evaluates the risk of these trajectories. Crucially, by leveraging backpropagation through the entire unrolled predictive model, agents can efficiently compute the gradient of future safety with respect to their current action, enabling a minimal and precise online safety correction. Integrated into a multi-agent training scheme, DMPS virtually eliminates collisions to less than 5.6% in high-density, mixed vehicle-robot traffic simulations, demonstrating state-of-the-art safety without compromising energy and traffic efficiency.
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