Semantic Segmentation on Automotive Radar Maps

Prophet R, Li G, Sturm C, Vossiek M (2019)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Event location: Paris

DOI: 10.1109/IVS.2019.8813808

Abstract

As radar sensors can measure an object's range and velocity with a high degree of precision, moving objects can be successfully classified, as well. Classifying stationary objects still needs a lot of research, however. In this paper, we use popular semantic segmentation networks in order to classify the vehicle's immediate infrastructure. To this end, a full 3D measurement is performed with a test vehicle equipped with four high resolution corner radar sensors. A preprocessed point cloud is transformed into various radar maps for input to a neural network. Simulations as well as real-world measurements show an overall intersection over union of 84 and 77%, respectively, as well as an overall accuracy of 95 and 90%, respectively, being a new benchmark for this young research field.

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How to cite

APA:

Prophet, R., Li, G., Sturm, C., & Vossiek, M. (2019). Semantic Segmentation on Automotive Radar Maps. In Proceedings of the IEEE Intelligent Vehicles Symposium. Paris.

MLA:

Prophet, Robert, et al. "Semantic Segmentation on Automotive Radar Maps." Proceedings of the IEEE Intelligent Vehicles Symposium, Paris 2019.

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