Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy-Reference-Cited by-同舟云学术

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Published:2021-04 Issue: Volume:68 Page:161-191
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Fernandes Duarte,Silva António,Névoa Rafael,Simões Cláudia,Gonzalez Dibet,Guevara Miguel,Novais Paulo,Monteiro João,Melo-Pinto Pedro

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Signal Processing,Software

Reference153 articles.

1. Global status report on road safety;Ghebreyesus;Geneva,2018

2. Looking back on 10 years of global road safety;Peden;Int. Health,2019

3. How safe is safe enough for self-driving vehicles?;Liu;Risk Anal.,2019

4. The global macroeconomic burden of road injuries: estimates and projections for 166 countries;Chen;Lancet Planet. Heal.,2019

5. T.Litman, “Autonomous vehicle implementation predictions implications for transport planning,” Victoria, Canada, 2020. [Online]. Available: https://www.vtpi.org/avip.pdf.

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