Semantic Segmentation-Based Occupancy Grid Map Learning With Automotive Radar Raw Data

Jin Y, Hoffmann M, Deligiannis A, Fuentes-Michel JC, Vossiek M (2023)

Publication Type: Journal article

Publication year: 2023

Journal

IEEE Transactions on Intelligent Vehicles IEEE

Pages Range: 1-16

DOI: 10.1109/TIV.2023.3322353

Abstract

Precise road scene understanding is of great essence to autonomous driving. As a widely used method for road scene understanding, occupancy grid mapping is leveraged to detect obstacles and predict drivable road areas. Because of its robustness under harsh conditions, low cost, and large perceptual range, radar sensor is becoming increasingly important to achieve various critical perception tasks. However, for radar-based occupancy grid mapping, current inverse sensor model ISM relies on detection data and is most hand-crafted. In this work, we propose a novel data-driven ISM that employs the range- Doppler matrix as the input. With a systematic evaluation and comparison of our model with classic, hand-crafted ISM and the data-driven, detection-based Occupancy Net using RADIal dataset, we find that data-driven models are far superior to their hand-crafted counterpart. Furthermore, although both data-driven models are on par within near range ($< 50 \,\mathrm{m}$), our model outperforms Occupancy Net by a large margin in far range ($ [50 \,\mathrm{m},100 \,\mathrm{m}]$). Specifically, our model has about a 0.3 and 0.4 improvement in distant range for the intersection over union IoU and $\mathrm{F}_{1}$ score, respectively. In addition, leveraging adjacent occupancy grid map prediction, we propose a radar-based occupancy flow to precisely distinguish moving objects.

Authors with CRIS profile

Yi Jin Lehrstuhl für Hochfrequenztechnik Marcel Hoffmann Lehrstuhl für Hochfrequenztechnik Martin Vossiek Lehrstuhl für Hochfrequenztechnik

Additional Organisation(s)

FAU Profilzentrum Licht.Materie.Quantentechnologien (FAU LMQ)

Involved external institutions

BMW AG - Bayerische Motoren Werke / BMW Group DE Germany (DE)

How to cite

APA:

Jin, Y., Hoffmann, M., Deligiannis, A., Fuentes-Michel, J.C., & Vossiek, M. (2023). Semantic Segmentation-Based Occupancy Grid Map Learning With Automotive Radar Raw Data. IEEE Transactions on Intelligent Vehicles, 1-16. https://doi.org/10.1109/TIV.2023.3322353

MLA:

Jin, Yi, et al. "Semantic Segmentation-Based Occupancy Grid Map Learning With Automotive Radar Raw Data." IEEE Transactions on Intelligent Vehicles (2023): 1-16.

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