PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning
End-to-end autonomous driving (E2E AD) systems integrate perception, prediction, and planning into a single differentiable architecture. While these models show great promise, their standard training often relies on output-only supervision, which can lead to weak gradients for the hidden layers of increasingly complex models. Recent works have integrated vision-language model (VLM) supervision for latent features to address this, yielding substantial empirical gains, yet leaving the underlying theoretical mechanisms poorly understood. Our investigation into this methodology reveals that the resulting performance gains stem not from VLM reasoning capabilities, as previously assumed, but ra...
| Authors | Volodymyr Havrylov, Faris Janjoš, Andreas Look et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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