An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles-Reference-Cited by-同舟云学术

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An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

Published:2024-07 Issue: Volume:133 Page:108550
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Thakur Abhishek^ORCID,Mishra Sudhansu Kumar^ORCID

Publisher

Elsevier BV

Reference189 articles.

1. On using a low-density Flash LiDAR for road vehicle tracking;A;ASME. J. Dyn. Sys., Meas., Control,2021

2. Custom based obstacle detection using yolo v3 for low flying drones,2021

3. A comprehensive review of recent advances on deep vision systems;Abbas;Artif. Intell. Rev.,2019

4. LiDAR point cloud compression, processing and learning for autonomous driving;Abbasi;IEEE Trans. Intell. Transport. Syst.,2022

5. November. The importance of signal pre-processing for machine learning: the influence of Data scaling in a driver identity classification;Abdennour,2021

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1. A systematic review and bibliometric analysis of electric cooking: evolution, emerging trends, and future research directions for sustainable development;Sustainable Energy Research;2024-06-27

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