Machine learning researchers must urgently work with policymakers to address growing risks from embodied AI by plugging gaps in existing frameworks.

We introduce URB, a standardized benchmark for evaluating MARL in urban routing with autonomous vehicles across 29 real-world traffic networks, revealing that current state-of-the-art algorithms struggle to outperform humans and scale effectively.