Abstract
Accurate and up-to-date digital road maps are the foundation of many applications, such as navigation and autonomous driving. Recently, the ubiquity of GPS devices in vehicular systems has led to an unprecedented amount of vehicle sensing data for map inference. Existing trajectory-based map generation methods are difficult to accurately generate parallel roads where the GPS positioning errors are large, and the sampling frequency is low. In this paper, we propose a novel method MPRG to discover parallel roads based on the differences between free and fixed trajectories from different types of vehicles. This method can serve as a plugin for any existing map generation method. MPRG extracts highly discriminative features by utilizing the spatial distribution and regional correlation information of trajectories from different vehicle types. Then, the multidimensional features are fed into an SVM classification model suitable for small sample to identify and generate the parallel roads. We apply MPRG to three advanced road generation methods using GPS data from Shenzhen. The results show that we can significantly improve the performance of parallel road generation.
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Acknowledgement
This study was funded by the National Natural Science Foundation of China (No. 62372443, No. 62376263), Shenzhen Industrial Application Projects of undertaking the National key R & D Program of China (No. CJGJZD20210408091600002).
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Han, B., Zhao, J., Gao, X., Ye, K., Zhang, F. (2024). MPRG: A Method for Parallel Road Generation Based on Trajectories of Multiple Types of Vehicles. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_25
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DOI: https://doi.org/10.1007/978-981-97-2262-4_25
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