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.

翻译：自动驾驶汽车与移动机器人在城市环境中的快速整合，为未来智能交通系统带来了严峻的安全挑战。本文聚焦于非管制交叉口动力学特性迥异的异构智能体间的复杂协调问题，提出了一种名为可微分模型预测安全性（DMPS）的创新框架。该框架将模型预测控制的预见能力嵌入数据驱动的端到端强化学习架构中。DMPS智能体通过学习获得隐式动力学模型，可基于自身动作预测未来轨迹。随后，经过训练的可微分安全性评判器对这些轨迹的风险进行评估。关键在于，通过利用贯穿整个展开预测模型的反向传播机制，智能体能够高效计算当前动作相对于未来安全性的梯度，从而实现对在线安全性的最小化精准修正。将DMPS集成至多智能体训练方案后，在高密度混合车辆-机器人交通仿真中，碰撞率被有效降至5.6%以下，在不牺牲能源效率与通行效率的前提下展现了当前最优的安全性。