LSTM-based System for Multiple Obstacle Detection using Ultra-wide Band Radar

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

2021

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.

Paper Citation

in Harvard Style

Mimouna A., Ben Khalifa A., Alouani I., Taleb-Ahmed A., Rivenq A. and Ben Amara N. (2021). LSTM-based System for Multiple Obstacle Detection using Ultra-wide Band Radar.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 418-425. DOI: 10.5220/0010386904180425

in Bibtex Style

@conference{icaart21,
author={Amira Mimouna and Anouar Ben Khalifa and Ihsen Alouani and Abdelmalik Taleb-Ahmed and Atika Rivenq and Najoua Ben Amara},
title={LSTM-based System for Multiple Obstacle Detection using Ultra-wide Band Radar},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={418-425},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010386904180425},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - LSTM-based System for Multiple Obstacle Detection using Ultra-wide Band Radar
SN - 978-989-758-484-8
AU - Mimouna A.
AU - Ben Khalifa A.
AU - Alouani I.
AU - Taleb-Ahmed A.
AU - Rivenq A.
AU - Ben Amara N.
PY - 2021
SP - 418
EP - 425
DO - 10.5220/0010386904180425