Multimodal Large Language Models (MLLMs) have shown outstanding capabilities
in many areas of multimodal reasoning. Therefore, we use the reasoning ability
of Multimodal Large Language Models for environment description and scene
understanding in complex transportation environments. In this paper, we propose
AccidentBlip2, a multimodal large language model that can predict in real time
whether an accident risk will occur. Our approach involves feature extraction
based on the temporal scene of the six-view surround view graphs and temporal
inference using the temporal blip framework through the vision transformer. We
then input the generated temporal token into the MLLMs for inference to
determine whether an accident will occur or not. Since AccidentBlip2 does not
rely on any BEV images and LiDAR, the number of inference parameters and the
inference cost of MLLMs can be significantly reduced, and it also does not
incur a large training overhead during training. AccidentBlip2 outperforms
existing solutions on the DeepAccident dataset and can also provide a reference
solution for end-to-end automated driving accident prediction.

本文提出了 AccidentBlip2，一种多模态大型语言模型，用于复杂交通环境中的环境描述和场景理解，并在实时预测事故风险方面表现出色，为自动驾驶事故预测提供了参考解决方案。