  Intelligent vehicles have demonstrated excellent capabilities in many
transportation scenarios. The inference capabilities of neural networks using
cameras limit the accuracy of accident detection in complex transportation
systems. This paper presents AccidentBlip2, a pure vision-based multi-modal
large model Blip2 for accident detection. Our method first processes the
multi-view images through ViT-14g and sends the multi-view features into the
cross-attention layer of Q-Former. Different from Blip2's Q-Former, our Motion
Q-Former extends the self-attention layer with the temporal-attention layer. In
the inference process, the queries generated from previous frames are input
into Motion Q-Former to aggregate temporal information. Queries are updated
with an auto-regressive strategy and are sent to a MLP to detect whether there
is an accident in the surrounding environment. Our AccidentBlip2 can be
extended to a multi-vehicle cooperative system by deploying Motion Q-Former on
each vehicle and simultaneously fusing the generated queries into the MLP for
auto-regressive inference. Our approach outperforms existing video large
language models in detection accuracy in both single-vehicle and multi-vehicle
systems.
