Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning

Martinez Garcia J, Prophet R, Fuentes MJC, Ebelt R, Vossiek M, Weber I (2019)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Event location: Detroit, USA

DOI: 10.1109/ICMIM.2019.8726704

Abstract

We introduce a method to classify ghost moving detections in automotive radar sensors for advanced driver assistance systems. A fully connected network is used to distinguish between real and false moving detections in the occupancy gridmaps. By using this architecture, we combine the local Doppler information, along with the spatial context of the surrounding scenario to classify the moving detections. A proof of concept experiment shows promising results with data from a test drive in an urban scenario.

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How to cite

APA:

Martinez Garcia, J., Prophet, R., Fuentes, M.J.C., Ebelt, R., Vossiek, M., & Weber, I. (2019). Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning. In Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2019). Detroit, USA, US.

MLA:

Martinez Garcia, Javier, et al. "Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning." Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2019), Detroit, USA 2019.

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