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Deep Learning-Based Vehicle Direction Detection

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 296))

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Abstract

Advanced driver-assistance systems (ADAS) play a significant role in reducing traffic fatalities and vehicular accidents. Among the many ADAS technologies is the blind spot alert monitor alerts you of a vehicle or an obstruction before you change lanes. This paper investigates extending the monitoring concept to detect if a car emerging from the blind spot is attempting to cut into your lane. In that respect, we would perceive the direction of the infringing vehicle. There are various means of sensing such a situation, including radar, lidar, camera, and sonar. We focus on using lidar and deep learning for this purpose in this paper. In particular, we present the experiments carried out to verify the concept, including details of the equipment, experimental data, and MATLAB Deep Learning for detection.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, School of Engineering and Computer Science, Oakland University, Rochester, MI, 48309, USA

    Nashwan J. Sebi & Ka C. Cheok

  2. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, 3-7-2 Kajinocho, Koganei, Tokyo, 184-8584, Japan

    Kazuyuki Kobayashi

Authors
  1. Nashwan J. Sebi
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  2. Kazuyuki Kobayashi
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  3. Ka C. Cheok
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Corresponding author

Correspondence to Nashwan J. Sebi .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Sebi, N.J., Kobayashi, K., Cheok, K.C. (2022). Deep Learning-Based Vehicle Direction Detection. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_28

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