Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance
The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total au...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/7/1/2 |
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| author | Alfredo Medina-Garcia Jonathan Duarte-Jasso Juan-Jose Cardenas-Cornejo Yair A. Andrade-Ambriz Marco-Antonio Garcia-Montoya Mario-Alberto Ibarra-Manzano Dora-Luz Almanza-Ojeda |
| author_facet | Alfredo Medina-Garcia Jonathan Duarte-Jasso Juan-Jose Cardenas-Cornejo Yair A. Andrade-Ambriz Marco-Antonio Garcia-Montoya Mario-Alberto Ibarra-Manzano Dora-Luz Almanza-Ojeda |
| author_sort | Alfredo Medina-Garcia |
| collection | DOAJ |
| description | The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements. |
| first_indexed | 2024-03-07T22:14:22Z |
| format | Article |
| id | doaj.art-d384b69c88874eafae461baadae66a7f |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-07T22:14:22Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-d384b69c88874eafae461baadae66a7f2024-02-23T15:34:23ZengMDPI AGSmart Cities2624-65112023-12-0171335010.3390/smartcities7010002Vision-Based Object Localization and Classification for Electric Vehicle Driving AssistanceAlfredo Medina-Garcia0Jonathan Duarte-Jasso1Juan-Jose Cardenas-Cornejo2Yair A. Andrade-Ambriz3Marco-Antonio Garcia-Montoya4Mario-Alberto Ibarra-Manzano5Dora-Luz Almanza-Ojeda6Electronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoElectronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoElectronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoElectronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoElectronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoElectronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoElectronics Engineering Department, DICIS, University of Guanajuato, Salamanca 36885, Guanajuato, MexicoThe continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.https://www.mdpi.com/2624-6511/7/1/2visual navigationobject classificationdriving assistanceoccupancy mapGPS pose |
| spellingShingle | Alfredo Medina-Garcia Jonathan Duarte-Jasso Juan-Jose Cardenas-Cornejo Yair A. Andrade-Ambriz Marco-Antonio Garcia-Montoya Mario-Alberto Ibarra-Manzano Dora-Luz Almanza-Ojeda Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance Smart Cities visual navigation object classification driving assistance occupancy map GPS pose |
| title | Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance |
| title_full | Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance |
| title_fullStr | Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance |
| title_full_unstemmed | Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance |
| title_short | Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance |
| title_sort | vision based object localization and classification for electric vehicle driving assistance |
| topic | visual navigation object classification driving assistance occupancy map GPS pose |
| url | https://www.mdpi.com/2624-6511/7/1/2 |
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