LSTM-based system for multiple obstacle detection using ultra-wide band radar
| العنوان: | LSTM-based system for multiple obstacle detection using ultra-wide band radar |
|---|---|
| المؤلفون: | Ihsen Alouani, Anouar Ben Khalifa, Najoua Essoukri Ben Amara, Amira Mimouna, Atika Rivenq, Abdelmalik Taleb-Ahmed |
| المساهمون: | Laboratory of Advanced Technology and Intelligent Systems (LATIS), Ecole Nationale d'Ingénieurs de Sousse (ENISo), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), COMmunications NUMériques - IEMN (COMNUM - IEMN), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA) |
| المصدر: | 13th International Conference on Agents and Artificial Intelligence, ICAART 2021 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Feb 2021, Online Streaming, Austria. pp.418-425, ⟨10.5220/0010386904180425⟩ 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Feb 2021, Vienna (Online Streaming), Austria. pp.418-425, ⟨10.5220/0010386904180425⟩ Scopus-Elsevier ICAART (2) |
| بيانات النشر: | HAL CCSD, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, UWB Radar, Ultra-wideband, law.invention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI.TRON]Engineering Sciences [physics]/Electronics, Intelligent Transportation Systems, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], Deep Learning, law, Obstacle, 11. Sustainability, Electronic engineering, [INFO]Computer Science [cs], Radar, LSTM, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Obstacle Detection |
| الوصف: | International audience; Autonomous vehicles present a promising opportunity in the future of transportation systems by providing road safety. As significant progress has been made in the automatic environment perception, the detection of road obstacles remains a major challenge. Thus, to achieve reliable obstacle detection, several sensors have been employed. For short ranges, the Ultra-Wide Band (UWB) radar is utilized in order to detect objects in the near field. However, the main challenge appears in distinguishing the real target’s signature from noise in the received UWB signals. In this paper, we propose a novel framework that exploits Recurrent Neural Networks (RNNs) with UWB signals for multiple road obstacle detection. Features are extracted from the time-frequency domain using the discrete wavelet transform and are forwarded to the Long short-term memory (LSTM) network. We evaluate our approach on the OLIMP dataset which includes various driving situations with complex environment and targets from several classes. The obtained results show that the LSTM-based system outperforms the other implemented related techniques in terms of obstacle detection. |
| اللغة: | English |
| DOI: | 10.5220/0010386904180425⟩ |
| DOI: | 10.5220/0010386904180425 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b102c7ec818658defb9c76651a9b2a2 https://hal.archives-ouvertes.fr/hal-03195216/file/Mimouna_ICAART2021.pdf |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....2b102c7ec818658defb9c76651a9b2a2 |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.5220/0010386904180425⟩ |
|---|