pith:REHXFOZW
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
A simple MLP using only past trajectories and velocity matches perception-based planners on nuScenes L2 error.
arxiv:2305.10430 v2 · 2023-05-17 · cs.CV
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Claims
Our simple method achieves similar end-to-end planning performance on the nuScenes dataset with other perception-based methods, reducing the average L2 error by about 20%.
That the MLP baseline is trained and evaluated under identical conditions and data splits as the perception-based competitors, with no hidden advantages from trajectory history alone.
A perception-free MLP reduces average L2 trajectory error by ~20% versus perception-based methods on nuScenes, suggesting current open-loop evaluation may reward trajectory mimicry over safe planning.
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| First computed | 2026-05-17T23:38:14.595898Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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