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doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving

Paper recorded by Signals 4 on 2026-09-29 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30

Category: cs.AI · 人工智能 · first seen 2026-09-30

Abstract

Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple stages of behavior, depend on future events, refer to surrounding agents or landmarks, and remain relevant as driving conditions evolve. Existing language-enabled driving datasets largely focus on short, localized interactions, leaving these longer-hori

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#20 most recent of 460 cs.AI papers we have recorded · ↑ newer: Gender bias across LLMs is common and highly heterogenous · ↓ older: FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal
Cite this page: doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving: the #20 most recent of 460 cs.AI papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/doplan-a-variable-horizon-dataset-for-multi-stage-language-conditioned-planning-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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