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

Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Abdelmalik Taleb-Ahmed, Atika Rivenq, Najoua Essoukri Ben Amara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
26 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages418-425
Volume2
ISBN (Electronic)9789897584848
DOIs
Publication statusPublished - 06 Feb 2021
Externally publishedYes
Event13th International Conference on Agents and Artificial Intelligence - virtual, online
Duration: 04 Feb 202106 Feb 2021
https://icaart.scitevents.org/?y=2021

Publication series

NameInternational Conference on Agents and Artificial Intelligence: Proceedings
ISSN (Electronic)2184-433X

Conference

Conference13th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART
Cityvirtual, online
Period04/02/202106/02/2021
Internet address

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