Abstract
Within the context of the environmental perception of autonomous vehicles (AVs), this paper establishes a sensor model based on the experimental sensor fusion of lidar and monocular cameras. The sensor fusion algorithm can map three-dimensional space coordinate points to a two-dimensional plane based on both space synchronization and time synchronization. The YOLO target recognition and density clustering algorithms obtain the data fusion containing the obstacles’ visual information and depth information. Furthermore, the experimental results show the high accuracy of the proposed sensor data fusion algorithm.
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Mei, P., Karimi, H.R., Ma, F., Yang, S., Huang, C. (2022). A Multi-sensor Information Fusion Method for Autonomous Vehicle Perception System. In: Paiva, S., et al. Science and Technologies for Smart Cities. SmartCity 360 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 442. Springer, Cham. https://doi.org/10.1007/978-3-031-06371-8_40
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DOI: https://doi.org/10.1007/978-3-031-06371-8_40
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