Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar
Wachtel D, Edler J, Schroder S, Queiroz S, Huber W (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2022-October
Pages Range: 866-872
Conference Proceedings Title: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Event location: Macau
ISBN: 9781665468800
DOI: 10.1109/ITSC55140.2022.9921852
Abstract
Advanced driver assistance systems (ADAS) are growing in quantity and complexity, requiring even more sensor effectiveness and computational resources. Even though cameras and LiDAR sensors can provide a steady solution in ideal conditions, they are unreliable for adverse weather conditions, such as rain and dense fog. Therefore, radar technology is used for those circumstances is an option to be used for those circumstances. However, its classification accuracy must be enhanced to compete with the other sensor types. This work presents a solution to enhance radar classification quality, adapting a Convolutional Neural Network (CNN) structure to the input of micro-Doppler images of vulnerable road users (VRUs), such as pedestrians, cyclists, and e-scooter drivers. Moreover, two CNNs were trained, each for a particular region of interest (ROIs), one in the near field of the radar until 15m, and the second one up to 25m. The proposed solution presents a promising overall accuracy for its first trial. Nevertheless, it must be further extended to more complex scenes, including more participants, obstacles, and weather conditions.
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Germany (DE)How to cite
APA:
Wachtel, D., Edler, J., Schroder, S., Queiroz, S., & Huber, W. (2022). Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 866-872). Macau, CN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Wachtel, Diogo, et al. "Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar." Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, Macau Institute of Electrical and Electronics Engineers Inc., 2022. 866-872.
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