SOURCE-LINKED INTELLIGENCE
NavSafe-$\infty$: Benchmarking Closed-Loop Driving Safety in Photorealistic Environments
End-to-end (E2E) driving policies have progressed rapidly on open-loop (OL) benchmarks, yet OL evaluation cannot reveal whether a policy withstands compounding errors, recovers from failures, or interacts safely with surrounding actors. We introduce NavSafe-$\infty$, a photorealistic closed-loop (CL) benchmark of 280 scenarios spanning 28 event types, each with success and failure criteria defined within a structured traffic-safety taxonomy, which yields category-level capability scores for Traffic Crashes, Vulnerable Road User Crashes, Traffic Violations, and Traffic Incidents. Evaluating 20
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T15:51:07.000Z
First collected: 2026-09-23T04:11:12.117Z. This is not the publication date.