Towards Robust Autonomous Driving: Conditional Multimodal Large Language Models for Fine-Grained Perception

Abstract

Multimodal large language models (MLLMs) have shown remarkable performance across various visual understanding tasks. However, most existing MLLMs still lack image detail perception, limiting their effectiveness in tasks that require detailed visual information. In this paper, we introduce Percept-DriveLM, a novel MLLM designed to tackle the fine-grained perception challenges in autonomous driving tasks. At the core of our model is the Visual Fusion Module, which integrates several innovative components: a dynamic resolution mechanism that combines both high and low resolution features, and an RoI conditional mechanism to incorporate object/region-level features identified by offline detectors, further refining the model's fine-grained perception abilities. Trained in a two-stage process, our model demonstrates exceptional performance, outperforming existing MLLMs with comparable parameter sizes and excelling in both autonomous driving perception and general vision-language tasks. The effectiveness of our approach is validated through extensive empirical studies. Code will be available at https://github.com/DebuggerSunfz/PerceptDriveLM.