Spatial Mapping of Motion Detection Zones Through the Fusion of Radar and Optical Data
DOI:
https://doi.org/10.5281/zenodo.20644196Keywords:
radar-camera fusion, motion detection, spatial mapping, occupancy grid, sensor reliability.Abstract
The objective of the study was to substantiate a model for the spatial mapping of motion detection zones based on the fusion of radar and optical data. The methodology encompassed bibliographic synthesis, logical modeling, sensor synchronization, projection of the data onto a common coordinate grid, and computation of a cell reliability index. The results are presented as a model that partitions space into stable, conditionally reliable, uncertain, and blind zones; in the worked example, the share of reliable coverage was 65%. The proposed approach can be applied in traffic monitoring, perimeter security, industrial safety, and the preliminary auditing of sensor system configurations.
References
Baek, S., Kim, J., & Yi, K. (2024). Robust tracking and detection based on radar camera fusion filtering in urban autonomous driving. Intelligent Service Robotics, 17, 1125–1141. https://doi.org/10.1007/s11370-024-00563-0
Cai, G., Chen, F., & Guo, E. (2024). IRBEVF-Q: Optimization of image–radar fusion algorithm based on bird's eye view features. Sensors, 24(14), 4602. https://doi.org/10.3390/s24144602
Cheng, L., Sengupta, A., & Cao, S. (2024). Deep learning-based robust multi-object tracking via fusion of mmWave radar and camera sensors. IEEE Transactions on Intelligent Transportation Systems, 25(11), 17218–17233. https://doi.org/10.1109/TITS.2024.3421339
Fent, F., Palffy, A., & Caesar, H. (2025). DPFT: Dual perspective fusion transformer for camera-radar-based object detection. IEEE Transactions on Intelligent Vehicles, 10(11), 4929–4941. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10769556
Jang, H., Kim, T., Ahn, K., Jeon, S., & Kang, Y. (2024). Dynamic occupancy grid map with semantic information using deep learning-based BEVFusion method with camera and LiDAR fusion. Sensors, 24(9), 2828. https://doi.org/10.3390/s24092828
Qian, H., Wang, M., Zhu, M., & Wang, H. (2025). A review of multi-sensor fusion in autonomous driving. Sensors, 25(19), 6033. https://doi.org/10.3390/s25196033
Sun, X., Jiang, Y., Qin, H., Li, J., & Ji, Y. (2024). Camera-radar fusion with radar channel extension and dual-CBAM-FPN for object detection. Sensors, 24(16), 5317. https://doi.org/10.3390/s24165317
Wang, H., Liu, J., Dong, H., & Shao, Z. (2025). A survey of the multi-sensor fusion object detection task in autonomous driving. Sensors, 25(9), 2794. https://doi.org/10.3390/s25092794
Xiao, Y., Liu, Y., Luan, K., Cheng, Y., Chen, X., & Lu, H. (2023). Deep LiDAR-radar-visual fusion for object detection in urban environments. Remote Sensing, 15(18), 4433. https://doi.org/10.3390/rs15184433
Yao, S., Guan, R., Huang, X., Li, Z., Sha, X., Yue, Y., & Yue, Y. (2024). Radar-camera fusion for object detection and semantic segmentation in autonomous driving: A comprehensive review. IEEE Transactions on Intelligent Vehicles, 9(1), 2094–2128. https://doi.org/10.1109/TIV.2023.3307157
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Copyright (c) 2025 Illia Bondariev

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