TY  - JOUR
PY  - 2024//
TI  - Vision-based object localization and classification for electric vehicle driving assistance
JO  - Smart cities (basel)
A1  - Medina-Garcia, Alfredo
A1  - Duarte-Jasso, Jonathan
A1  - Cardenas-Cornejo, Juan-Jose
A1  - Andrade-Ambriz, Yair A.
A1  - Garcia-Montoya, Marco-Antonio
A1  - Ibarra-Manzano, Mario-Alberto
A1  - Almanza-Ojeda, Dora-Luz
SP  - 33
EP  - 50
VL  - 7
IS  - 1
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2624-6511
UR  - http://dx.doi.org/10.3390/smartcities7010002
ID  - ref1
ER  -