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Enhanced Autonomous Driving Through Improved 3D Objects Detection

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Book cover Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 449))

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Abstract

The detection of 3D objects is fundamental in the field of autonomous driving. The involved computations consist of sets of 3D bounding boxes that determine specific significant objects. The existing scientific contributions generally do not report applied studies that assess the reliability of 3D objects detection considering various weather conditions, including adverse scenarios like heavy rain and thick fog, and also other relevant problematic use cases. This paper presents an applied research process that describes the core of a 3D objects detection system, which considers two particular road topologies, a roundabout and a T-junction. The experimental data is collected through a partnership with several car manufacturers.

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Author information

Authors and Affiliations

  1. Transilvania University of Brasov, Brasov, Romania

    Razvan Bocu

  2. Caucasus University, Tbilisi, Georgia

    Maksim Iavich

Authors
  1. Razvan Bocu
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  2. Maksim Iavich
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Correspondence to Razvan Bocu .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Bocu, R., Iavich, M. (2022). Enhanced Autonomous Driving Through Improved 3D Objects Detection. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-030-99584-3_6

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