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 language | English |
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Title of host publication | Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021 |
Editors | Ana Paula Rocha, Luc Steels, Jaap van den Herik |
Publisher | SciTePress |
Pages | 418-425 |
Volume | 2 |
ISBN (Electronic) | 9789897584848 |
DOIs | |
Publication status | Published - 06 Feb 2021 |
Externally published | Yes |
Event | 13th International Conference on Agents and Artificial Intelligence - virtual, online Duration: 04 Feb 2021 → 06 Feb 2021 https://icaart.scitevents.org/?y=2021 |
Publication series
Name | International Conference on Agents and Artificial Intelligence: Proceedings |
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ISSN (Electronic) | 2184-433X |
Conference
Conference | 13th International Conference on Agents and Artificial Intelligence |
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Abbreviated title | ICAART |
City | virtual, online |
Period | 04/02/2021 → 06/02/2021 |
Internet address |