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

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.
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Submitted on : Saturday, April 10, 2021 - 6:56:37 PM
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Amira Mimouna, Anouar Ben Khalifa, Ihsen Alouani, Abdelmalik Taleb-Ahmed, Atika Rivenq, et al.. LSTM-based system for multiple obstacle detection using ultra-wide band radar. 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Feb 2021, Online Streaming, Austria. pp.418-425, ⟨10.5220/0010386904180425⟩. ⟨hal-03195216⟩



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