Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding

• URL: http://arxiv.org/abs/2511.08978v1
• Date: Thu, 13 Nov 2025 01:23:24 GMT
• Title: Spatio-Temporal Data Enhanced Vision-Language Model for Traffic Scene Understanding
• Authors: Jingtian Ma, Jingyuan Wang, Wayne Xin Zhao, Guoping Liu, Xiang Wen,
• Abstract summary: Traffic Scene Understanding (TSU) aims to provide a comprehensive description of the traffic scene.<n>Recent research often treats as common image understanding task, ignoring the intertemporal challenges.<n>This is the first attempt to integratetemporal information into vision models.
• Score: 49.748517517482014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Nowadays, navigation and ride-sharing apps have collected numerous images with spatio-temporal data. A core technology for analyzing such images, associated with spatiotemporal information, is Traffic Scene Understanding (TSU), which aims to provide a comprehensive description of the traffic scene. Unlike traditional spatio-temporal data analysis tasks, the dependence on both spatio-temporal and visual-textual data introduces distinct challenges to TSU task. However, recent research often treats TSU as a common image understanding task, ignoring the spatio-temporal information and overlooking the interrelations between different aspects of the traffic scene. To address these issues, we propose a novel SpatioTemporal Enhanced Model based on CILP (ST-CLIP) for TSU. Our model uses the classic vision-language model, CLIP, as the backbone, and designs a Spatio-temporal Context Aware Multiaspect Prompt (SCAMP) learning method to incorporate spatiotemporal information into TSU. The prompt learning method consists of two components: A dynamic spatio-temporal context representation module that extracts representation vectors of spatio-temporal data for each traffic scene image, and a bi-level ST-aware multi-aspect prompt learning module that integrates the ST-context representation vectors into word embeddings of prompts for the CLIP model. The second module also extracts low-level visual features and image-wise high-level semantic features to exploit interactive relations among different aspects of traffic scenes. To the best of our knowledge, this is the first attempt to integrate spatio-temporal information into visionlanguage models to facilitate TSU task. Experiments on two realworld datasets demonstrate superior performance in the complex scene understanding scenarios with a few-shot learning strategy.

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