TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains. We present TerraZero, a procedural driving simulator and self-play training stack. A configurable C engine runs simulation on the CPU and policy inference on the GPU over a zero-copy path, sustaining 1.3M agent-steps per se

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ethics.ai (14 July 2026), “TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale,” evidence record 10446, https://ethics.ai/record/10446 (originally published by arXiv cs.AI).

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