🤖 AI Summary
This work addresses the challenge of coordinating autonomous vehicles and mobile robots with heterogeneous dynamics in high-density, unsignalized urban intersections. To this end, the authors propose a Differentiable Model Predictive Safety (DMPS) framework that integrates the foresight of model predictive control into end-to-end reinforcement learning. DMPS jointly optimizes latent dynamical trajectory prediction and a differentiable safety critic, and—uniquely—enables gradient-based safety guidance directly in the action space. Experimental results demonstrate that DMPS reduces collision rates to below 5.6% in mixed-traffic simulations while simultaneously maintaining energy efficiency and throughput, thereby significantly enhancing real-time collision avoidance capabilities in heterogeneous multi-agent systems.
📝 Abstract
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.