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。

AccidentBlip2: 多视角运动检测的事故检测

AccidentBlip2: Accident Detection With Multi-View MotionBlip2

Rapid progress in machine learning and artificial intelligence (AI) has
brought increasing attention to the potential impacts of AI technologies on
society. In this paper we discuss one such potential impact: the problem of
accidents in machine learning systems, defined as unintended and harmful
behavior that may emerge from poor design of real-world AI systems. We present
a list of five practical research problems related to accident risk,
categorized according to whether the problem originates from having the wrong
objective function ("avoiding side effects" and "avoiding reward hacking"), an
objective function that is too expensive to evaluate frequently ("scalable
supervision"), or undesirable behavior during the learning process ("safe
exploration" and "distributional shift"). We review previous work in these
areas as well as suggesting research directions with a focus on relevance to
cutting-edge AI systems. Finally, we consider the high-level question of how to
think most productively about the safety of forward-looking applications of AI.

讨论机器学习与人工智能技术对社会潜在影响的一个问题：机器学习系统中的意外事故风险和如何抵御。我们提出了五个与事故风险相关的实际研究问题，涉及到错误的目标函数、过于昂贵的监督、安全探索和分布变化等方面。最后，思考了如何更具生产力地思考人工智能前瞻性应用的安全问题。