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 atthis https URL.
View on arXiv@article{wu2025_2309.04379,
title={ Language Prompt for Autonomous Driving },
author={ Dongming Wu and Wencheng Han and Yingfei Liu and Tiancai Wang and Cheng-zhong Xu and Xiangyu Zhang and Jianbing Shen },
journal={arXiv preprint arXiv:2309.04379},
year={ 2025 }
}