Ensuring adherence to traffic sign regulations is essential for both human and autonomous vehicle navigation. While current online mapping solutions often prioritize the construction of the geometric and connectivity layers of HD maps, overlooking the construction of the traffic regulation layer within HD maps. Addressing this gap, we introduce MapDR, a novel dataset designed for the extraction of Driving Rules from traffic signs and their association with vectorized, locally perceived HD Maps. MapDR features over 10,000 annotated video clips that capture the intricate correlation between traffic sign regulations and lanes. Built upon this benchmark and the newly defined task of integrating traffic regulations into online HD maps, we provide modular and end-to-end solutions: VLE-MEE and RuleVLM, offering a strong baseline for advancing autonomous driving technology. It fills a critical gap in the integration of traffic sign rules, contributing to the development of reliable autonomous driving systems. Code is available at https://github.com/MIV-XJTU/MapDR.

Recently, more and more large pre-trained models have emerged. Several parameter-efficient tuning methods have been studied to transfer the prior knowledge of the pre-trained models to specific downstream tasks and achieve promising results. This paper proposes a simple yet effective method called Atten-Adapter. To the best of our knowledge, this is the first work that utilizes attention with learnable parameters as the internal structure of the adapter in the field of fine-tuning. The attention-based adapter can provide better information fusion ability and pay more attention to the global features compared to the MLP-based adapter. As a plug-and-play module, Atten-Adapter can be easily adapted to different types of vision models such as ConvNets and Transformer architectures in different tasks like classification and segmentation. Moreover, we demonstrate the generality of our proposed adapters by conducting experiments on language models. With small amounts of tunable parameters, our method achieves significant improvements compared to the previous state-of-the-art methods.