TY - JOUR
T1 - Enhancing Electric Vehicle Navigation with 3D Vision-Based Object Localization and Classification
AU - Ji-eun Choi
AU - Hyejin Park
AU - Min-seo Kang
AU - Seung-ho Lee
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2024
VL - 3
IS - 2
SP - 79
N2 - Ongoing progress in intelligent systems and state-of-the-art technology has significantly shaped the evolution of intelligent vehicles. In recent years, the integration of multiple sensors into automobiles has enhanced and expanded advanced driver-assistance systems (ADAS), bringing the goal of full autonomy closer to reality. Despite existing self-driving techniques and implementations, autonomous driving remains an unresolved research challenge that must ensure driver safety and system dependability. This study uses images from two cameras and Global Positioning System (GPS) data to introduce a 3D vision-based approach for object localization and classification, aimed at supporting vehicle navigation. The experimental setup consists of a prototype two-seater electric vehicle, specifically developed and built for campus navigation under controlled driving conditions. At the same time, color and depth images from the main camera are fused to derive 2D features, which are then projected back into 3D space. Road detection combined with depth information isolates point clouds corresponding to objects, enabling the creation of an environmental occupancy map. A convolutional neural network was trained to categorize common urban objects within the color images. Experimental evaluations confirm the accuracy of vehicle and object positioning within the occupancy map across various scenarios, strengthening the visually derived vehicle position through integration with GPS measurements. 
UR - https://isespub.com/h180832004
ER -