Herding End-to-End Autonomous Driving via Neuro-Symbolic Safety Guards
Neuro-symbolic guards significantly boost autonomous safety by enforcing hard traffic rules on black-box AI agents.
Modern end-to-end driving agents can achieve high average performance yet still violate basic traffic rules that a human driver would never miss. The reason is structural: they learn statistical patterns rather than the physical conditions that guarantee safe driving, leaving the...
ArXiv CS.RO
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By layering formal symbolic rules over opaque neural networks, this approach provides a crucial safety net for autonomous systems without requiring complex model retraining. It successfully bridges the gap between high-performance statistical learning and the rigid, non-negotiable requirements of real-world traffic safety.
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