DriveMM: All-in-One Large Multimodal Model for Autonomous Driving

• URL: http://arxiv.org/abs/2412.07689v3
• Date: Fri, 13 Dec 2024 08:13:44 GMT
• Title: DriveMM: All-in-One Large Multimodal Model for Autonomous Driving
• Authors: Zhijian Huang, Chengjian Feng, Feng Yan, Baihui Xiao, Zequn Jie, Yujie Zhong, Xiaodan Liang, Lin Ma,
• Abstract summary: DriveMM is a large multimodal model designed to process diverse data inputs, such as images and multi-view videos, while performing a broad spectrum of autonomous driving tasks.<n>We conduct evaluations on six public benchmarks and undertake zero-shot transfer on an unseen dataset, where DriveMM achieves state-of-the-art performance across all tasks.
• Score: 63.882827922267666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despite the advancements, current data-driven AD approaches tend to concentrate on a single dataset and specific tasks, neglecting their overall capabilities and ability to generalize. To bridge these gaps, we propose DriveMM, a general large multimodal model designed to process diverse data inputs, such as images and multi-view videos, while performing a broad spectrum of AD tasks, including perception, prediction, and planning. Initially, the model undergoes curriculum pre-training to process varied visual signals and perform basic visual comprehension and perception tasks. Subsequently, we augment and standardize various AD-related datasets to fine-tune the model, resulting in an all-in-one LMM for autonomous driving. To assess the general capabilities and generalization ability, we conduct evaluations on six public benchmarks and undertake zero-shot transfer on an unseen dataset, where DriveMM achieves state-of-the-art performance across all tasks. We hope DriveMM as a promising solution for future end-to-end autonomous driving applications in the real world. Project page with code: https://github.com/zhijian11/DriveMM.

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