  Recent multimodal large language models (MLLM) such as GPT-4o and GPT-4v have
shown great potential in autonomous driving. In this paper, we propose a
cross-domain few-shot in-context learning method based on the MLLM for
enhancing traffic sign recognition (TSR). We first construct a traffic sign
detection network based on Vision Transformer Adapter and an extraction module
to extract traffic signs from the original road images. To reduce the
dependence on training data and improve the performance stability of
cross-country TSR, we introduce a cross-domain few-shot in-context learning
method based on the MLLM. To enhance MLLM's fine-grained recognition ability of
traffic signs, the proposed method generates corresponding description texts
using template traffic signs. These description texts contain key information
about the shape, color, and composition of traffic signs, which can stimulate
the ability of MLLM to perceive fine-grained traffic sign categories. By using
the description texts, our method reduces the cross-domain differences between
template and real traffic signs. Our approach requires only simple and uniform
textual indications, without the need for large-scale traffic sign images and
labels. We perform comprehensive evaluations on the German traffic sign
recognition benchmark dataset, the Belgium traffic sign dataset, and two
real-world datasets taken from Japan. The experimental results show that our
method significantly enhances the TSR performance.
