A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar

Juntao Guo; Zhaocheng Yang; Ping Chu; Jianhua Zhou

doi:10.1117/12.3022970

28 March 2024 A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar

Juntao Guo, Zhaocheng Yang, Ping Chu, Jianhua Zhou

Author Affiliations +

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130911L (2024) https://doi.org/10.1117/12.3022970
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar is presented in this paper. The main task is to detect whether there are vehicles approaching on both rear sides of the vehicle, and to give an early warning when the relative distance between the two vehicles is less than the early warning range. The difficulty in accurately detecting an approaching target vehicle lies in the fact that the vehicle is driving in a complex traffic scene and there are metal roadblocks in the road environments. In this paper, firstly, in order to reduce the complexity, the cascaded distance and angle of the potential target calculation is adopted to achieve the localization of the potential target. Then, rough recognition of vehicle targets and roadblocks is performed by clustering algorithm and structural features extraction. Since the features of some of the roadblocks are similar to those of the vehicle targets, secondary recognition is required to classify the vehicle targets and roadblocks by the target motion trend and trajectory length. The experimental results show that the average detection rate is 91.13% and the average false detection rate is 0.48% in different traffic scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Juntao Guo, Zhaocheng Yang, Ping Chu, and Jianhua Zhou "A low-complexity rear vehicle detection approach based on a dual-channel millimeter-wave radar", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130911L (28 March 2024); https://doi.org/10.1117/12.3022970

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