A machine learning joint lidar and radar classification system in urban automotive scenarios
| Autor: | R. Pérez, F. Schubert, R. Rasshofer, E. Biebl |
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| Jazyk: | German<br />English |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Advances in Radio Science, Vol 17, Pp 129-136 (2019) |
| Druh dokumentu: | article |
| ISSN: | 1684-9965 1684-9973 |
| DOI: | 10.5194/ars-17-129-2019 |
| Popis: | This work presents an approach to classify road users as pedestrians, cyclists or cars using a lidar sensor and a radar sensor. The lidar is used to detect moving road users in the surroundings of the car. A 2-dimensional range-Doppler window, a so called region of interest, of the radar power spectrum centered at the object's position is cut out and fed into a convolutional neural network to be classified. With this approach it is possible to classify multiple moving objects within a single radar measurement frame. The convolutional neural network is trained using data gathered with a test vehicle in real urban scenarios. An overall classification accuracy as high as 0.91 is achieved with this approach. The accuracy can be improved to 0.94 after applying a discrete Bayes filter on top of the classifier. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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