TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, however, is expensive across all stages: collecting and labeling millions of driving frames is costly, and closed-loop RL on images is bottlenecked by the per-step cost of photorealistic rendering plus a forward pass through a large vision backbone. Self-play in vectorized simulators changes the economics: millions of rollout steps per second, and a state distribution naturally rich in collisions, near-misses, and recoveries that no driving log contains. Our approach exploits this asymmetry by decoupling learning to drive from learning to see. We ...
| Authors | Zikang Xiong, Weixin Li, Zhouchonghao Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 June 2026 |
| Last updated at source | 15 July 2026 |
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