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arXiv · 2605.24531

NudgeVAD: Language-Nudged End-to-End Driving via FiLM Residuals

Chieh-Chi Yang·Yu-Hsiang Chen·Yi-Ting Chen
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Abstract

Natural-language instructions promise controllable end-to-end driving, but their benefit can be hidden when planners already receive reliable high-level commands. We propose NudgeVAD, a frozen-planner residual framework that uses language as a calibrated nudge to a VAD trajectory. With identity-initialized FiLM and a zero-initialized residual head, NudgeVAD is equivalent to the frozen planner at initialization, so learned deviations arise only from language-conditioned residuals. We evaluate NudgeVAD along a command-reliability axis. With reliable commands, language improves the initial planner but becomes nearly redundant once compared against VAD-FT (UNCOND), a compute-matched VAD model fine-tuned without language. With random commands, however, language becomes essential: detaching text degrades ADE6s to 3.166 m, while NudgeVAD with text recovers 2.806 m and outperforms VAD-FT (UNCOND) by 0.312 m. These results show that language is not universally additive; it is most valuable when the categorical command channel is unreliable.

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Chieh-Chi Yang, Yu-Hsiang Chen, Yi-Ting Chen. 2026-05-23. NudgeVAD: Language-Nudged End-to-End Driving via FiLM Residuals. https://arxiv.org/abs/2605.24531

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