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Language Prompt for Autonomous Driving

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arxiv 2309.04379 v2 pith:CEN4AWJK submitted 2023-09-08 cs.CV

Dongming Wu , Wencheng Han , Yingfei Liu , Tiancai Wang , Cheng-zhong Xu , Xiangyu Zhang , Jianbing Shen This is my paper · ORCID
classification cs.CV
keywords languagedrivingpromptcommunitydatanamednupromptobject
verification ladder T0 review T1 audit T2 compute T3 formal
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A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of using language prompts in driving scenarios is stuck in a bottleneck due to the scarcity of paired prompt-instance data. To address this challenge, we propose the first object-centric language prompt set for driving scenes within 3D, multi-view, and multi-frame space, named NuPrompt. It expands nuScenes dataset by constructing a total of 40,147 language descriptions, each referring to an average of 7.4 object tracklets. Based on the object-text pairs from the new benchmark, we formulate a novel prompt-based driving task, \ie, employing a language prompt to predict the described object trajectory across views and frames. Furthermore, we provide a simple end-to-end baseline model based on Transformer, named PromptTrack. Experiments show that our PromptTrack achieves impressive performance on NuPrompt. We hope this work can provide some new insights for the self-driving community. The data and code have been released at https://github.com/wudongming97/Prompt4Driving.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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