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Driving on Memory

Paper recorded by Signals 4 on 2026-08-31 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

End-to-end autonomous driving models plan future trajectories from raw sensor input. While earlier driving benchmarks often measured deviation from the human trajectory, current benchmarks such as NAVSIM and Bench2Drive evaluate models with richer simulation-based metrics intended to capture safe and compliant driving. A high benchmark score should reflect that a model can understand the scene in

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#183 most recent of 215 cs.LG papers we have recorded · ↑ newer: Normalized Low-Rank Adaptation · ↓ older: Learning the Geometry of Admissible Hypotheses through Inductive Bias
Cite this page: Driving on Memory: the #183 most recent of 215 cs.LG papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/driving-on-memory.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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