MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding

Abstract

Vision-language generative AI has demonstrated re-markable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However, current benchmark datasets lack multi-modal point cloud, image, and language data pairs. Recent approaches utilize visual instruction learning and cross-modal prompt engineering to expand vision-language models into this domain. In this paper, we pro-pose a new vision-language benchmark that can be used to finetune traffic and HD map domain-specific foundation models. Specifically, we annotate and leverage large-scale, broad-coverage traffic and map data extracted from huge HD map annotations, and use CLIP and LLaMA-2 / Vi-cuna to finetune a baseline model with instruction-following data. Our experimental results across various algorithms reveal that while visual instruction-tuning large language models (LLMs) can effectively learn meaningful represen-tations from MAPLM-QA, there remains significant room for further advancements. To facilitate applying LLMs and multi-modal data into self-driving research, we will release our visual-language QA data, and the baseline models at GitHub.com/LLVM-AD/MAPLM.