Advances in Object Detection for Intelligent Driving

Xuntong Hong

2025

Abstract

Intelligent driving is at the forefront of modern transportation technology, with target detection playing a pivotal role in the safe and efficient operation of autonomous driving systems. This paper reviews the latest advancements in target detection for intelligent driving, focusing on the challenges posed by external factors such as weather conditions, illumination variations, and traffic density, as well as internal factors related to sensor technology. The paper highlights the importance of multi-sensor fusion, combining data from cameras, LiDAR, and millimeter-wave radar, to enhance detection accuracy and robustness. It also provides an in-depth analysis of popular target detection methods, particularly the You Only Look Once (YOLO) family of models, which have demonstrated significant improvements in real-time detection and accuracy. Other methods, such as Faster R-CNN, Single Shot Multibox Detector (SSD), and RetinaNet, are also discussed, emphasizing their strengths and limitations in intelligent driving applications. Despite progress, challenges remain, including robustness in complex environments, small object detection, and balancing accuracy with real-time performance. Future directions include multimodal data fusion, unsupervised learning, and hardware acceleration to further improve target detection capabilities. The advancements in sensor technology, deep learning, and computational power will drive the continued evolution of intelligent driving systems.

Paper Citation

in Harvard Style

Hong X. (2025). Advances in Object Detection for Intelligent Driving. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 77-81. DOI: 10.5220/0013679000004670

in Bibtex Style

@conference{icdse25,
author={Xuntong Hong},
title={Advances in Object Detection for Intelligent Driving},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={77-81},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013679000004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Advances in Object Detection for Intelligent Driving
SN - 978-989-758-765-8
AU - Hong X.
PY - 2025
SP - 77
EP - 81
DO - 10.5220/0013679000004670
PB - SciTePress