The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness. In this paper, we introduce Dolphins, a novel vision-language model architected to imbibe human-like driving abilities. Dolphins is adept at processing multimodal inputs comprising video (or image) data, text instructions, and historical control signals to generate informed outputs corresponding to the provided instructions. Building upon the open-sourced pretrained Vision-Language Model, OpenFlamingo, we tailored Dolphins to the driving domain by constructing driving-specific instruction data and conducting instruction tuning. Through the utilization of the BDD-X dataset, we designed and consolidated four distinct AV tasks into Dolphins to foster a holistic understanding of intricate driving scenarios. As a result, the distinctive features of Dolphins are delineated into two dimensions: (1) the ability to provide a comprehensive understanding of complex and long-tailed open-world driving scenarios and solve a spectrum of AV tasks, and (2) the emergence of human-like capabilities including gradient-free rapid learning and adaptation via in-context learning, reflection and error recovery, and interoperability.
In this repo, we provide Dolphins code. This codebase is under MIT License.
- [TBD] We will soon release our code, model weight, dataset, and benchmark.
- [2023.12.3] We release the paper and the webpage of our project.
If you find our work useful, please consider citing us!
@misc{ma2023dolphins,
title={Dolphins: Multimodal Language Model for Driving},
author={Yingzi Ma and Yulong Cao and Jiachen Sun and Marco Pavone and Chaowei Xiao},
year={2023},
eprint={2312.00438},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
We thank the OpenFlamingo team and Otter team for their great contribution to the open-source community.