Abstract
Multi-sensor data fusion is an emerging technology, which has been widely used in medical diagnosis, remote sensing, inertial navigation and many other fields. What’s more, the implementation and application of automatic driving system rely heavily on target detection technology. Due to the high mobility and unpredictability of vehicle-mounted equipment, for automatic vehicles, it is arduous to achieve real-time and accurate vehicle target detection by a single sensor means, thus it is difficult to reliably guarantee the safety and stability. This paper proposes a novel object detection method based on a multi-sensor fusion mechanism, which considers the real-time sensing data from two types of sensors including radar and camera. It collects multi-vehicle speed and position information efficiently and reliably. Then, it filters and integrates data according to Extended Kalman Filter, Data Association Filter and some other methods. Furthermore, vehicle-borne equipment makes intelligent decision based on the data. In addition to theoretical support, the designed simulation results also show that the multi-sensor fusion mechanism can detect target vehicles efficiently and accurately, and it has superiority in the stability and accuracy of perception than single sensor sensing method.
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Acknowledgement
This research was supported by the National Key Research and Development Program of China under Grant No. 2017YFB0102502, 2017YFC0804803 and 2018YFB1600500, the Bei**g Municipal Natural Science Foundation under Grant No. L191001, the National Natural Science Foundation of China under Grant No. U20A20155 and 61822101, the Newton Advanced Fellow-ship under Grant No. 62061130221.
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Duan, X. et al. (2021). Multi-sensor Fusion Detection Method for Vehicle Target Based on Kalman Filter and Data Association Filter. In: Sun, X., Zhang, X., **a, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-78618-2_36
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DOI: https://doi.org/10.1007/978-3-030-78618-2_36
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