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| Temporal Vector Map Perception with Enhanced Query and Map Prior |
| LIN Yutong1, YUAN Wei2,3,4, ZHUANG Hanyang5, QIAN Yeqiang1,6 |
1. School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240;
2. School of Geospatial Artificial Intelligence, East China Normal University, Shanghai 200241;
3. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241;
4. Key Laboratory of Spatial-Temporal Big Data Analysis and App-lication of Natural Resources in Megacities, Ministry of Na-tural Resources, East China Normal University, Shanghai 200241;
5. Global College, Shanghai Jiao Tong University, Shanghai 200240;
6. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240 |
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Abstract Online construction of vectorized high-definition(HD) map is one of the critical tasks in autonomous driving system. However, the prediction performance of the existing methods is reduced under complex conditions, such as occlusion and low illumination. To address this issue, a method for temporal vector map perception with enhanced query and map prior(QPMap) is proposed. First, a Transformer-based standard definition map enhancement module and a rasterized high definition map initialization module are designed to enhance environmental understanding in complex scenarios. Second, an instance-level query encoding strategy is designed. The detection results from previous frames are introduced as temporal query priors into the current perception process to improve prediction stability and temporal consistency. Finally, a multi-scale decoder and a direction vector loss function are utilized to refine query representations. Thus, vectorized map elements are predicted accurately. A real-world dataset is constructed and a fisheye camera adaptation strategy is developed to validate the applicability of QPMap in actual autonomous driving scenarios. Experiments on multiple public datasets and a self-collected dataset demonstrate that QPMap achieves superior map construction performance while maintaining good real-time performance. Moreover, QPMap effectively improves the accuracy and robustness of online HD map construction.
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Received: 16 March 2026
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| Fund:National Natural Science Foundation of China(No.62473253,62573295), Major Project of 2025 Key Research and Development Program of Ningxia Hui Autonomous Region(No.2025BEG01002) |
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Corresponding Authors:
YUAN Wei, Ph.D., assistant professor. His research interests include end-to-end autonomous driving, reinforcement learning for autonomous driving, and mobile robot applications.
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About author:: LIN Yutong, Master student. His research interests include autonomous driving, computer vision, and visual localization and navigation.
ZHUANG Hanyang, Ph.D., associate professor. His research interests include AD/ADAS system design, high-precision localization, environment perception, and cooperative driving.
QIAN Yeqiang, Ph.D., associate profe-ssor. His research interests include computer vision, pattern recognition, machine learning and their applications. |
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2026, Vol. 39 
