We Need to Talk: Identifying and Overcoming Communication-Critical Scenarios for Self-Driving

Abstract

In this work, we consider the task of collision-free trajectory planning for connected self-driving vehicles. We specifically consider communication-critical situations--situations where single-agent systems have blindspots that require multi-agent collaboration. To identify such situations, we propose a method which (1) simulates multi-agent perspectives from real self-driving datasets, (2) finds scenarios that are challenging for isolated agents, and (3) augments scenarios with adversarial obstructions. To overcome these challenges, we propose to extend costmap-based trajectory evaluation to a distributed multi-agent setting. We demonstrate that our bandwidth-efficient, uncertainty-aware method reduces collision rates by up to 62.5% compared to single agent baselines.