Titre :

LSTM-based system for multiple obstacle detection using ultra-wide band radar

Auteur(s) :

Mimouna, Amira [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Ben Khalifa, Anouar [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]
Alouani, Lihsen [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Tahleb Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Rivenq, Atika [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Ben Amara, Najoua [Auteur]
Laboratory of Advanced Technology and Intelligent Systems [LATIS]

Titre de la manifestation scientifique :

13th International Conference on Agents and Artificial Intelligence, ICAART 2021

Ville :

Vienna (Online Streaming)

Pays :

Autriche

Date de début de la manifestation scientifique :

2021-02-04

Titre de l’ouvrage :

13th International Conference on Agents and Artificial Intelligence, ICAART 2021

Titre de la revue :

Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021

Éditeur :

SCITEPRESS - Science and Technology Publications

Date de publication :

2021

Mot(s)-clé(s) en anglais :

Obstacle Detection
UWB Radar
Deep Learning
LSTM
Intelligent Transportation Systems

Discipline(s) HAL :

Sciences de l'ingénieur [physics]
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Electronique
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL