CORTEXA
← Browse
crossrefApplied Sciences2025-02-14Cited by 2

Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach

Przemysław Sekuła, Narjes Shayesteh, Qinglian He, Sara Zahedian, Rodrigo Moscoso, Michał Cholewa

This paper presents the results of the Move Over law compliance study. This study was carried out for The Federal Highway Administration in cooperation with ten State Highway agencies that provided the data (video recordings). This paper describes an outline of the system that was invented, developed, and applied to determine Move Over law compliance, as well as the initial analysis of the impact of various factors on compliance. In order to carry out the analysis, we processed 68 videos that contained over 33,000 vehicles. The median compliance with the Move Over law was 42.5% and varied heavily depending on diverse factors. This study makes two key contributions: first, it introduces an automated deep learning-based system that detects and evaluates Move Over law compliance by leveraging object detection and tracking technologies. Second, it presents a large-scale, multi-state compliance assessment, providing new empirical insights into driver behavior across various incident conditions. These findings offer a data-driven foundation for refining Move Over laws, enhancing public awareness efforts, and improving enforcement strategies.

View free PDFSource page

Related papers

crossrefApplied Sciences2025-05-12Cited by 7

Real-Time Accurate Determination of Table Tennis Ball and Evaluation of Player Stroke Effectiveness with Computer Vision-Based Deep Learning

Zilin He, Zeyi Yang, Jiarui Xu, Hongyu Chen, Xuanfeng Li, Anzhe Wang, et al.

The adoption of artificial intelligence (AI) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision…

View free PDFSource page
crossrefApplied Sciences2025-07-30Cited by 19

A Review of Computer Vision and Deep Learning Applications in Crop Growth Management

Zhijie Cao, Shantong Sun, Xu Bao

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agricultur…

View free PDFSource page
crossrefApplied Sciences2025-06-17Cited by 3

Resilient AI in Therapeutic Rehabilitation: The Integration of Computer Vision and Deep Learning for Dynamic Therapy Adaptation

Egidia Cirillo, Claudia Conte, Alberto Moccardi, Mattia Fonisto

Resilient artificial intelligence (Resilient AI) is relevant in many areas where technology needs to adapt quickly to changing and unexpected conditions, such as in the medical, environmental, security, and agrifood sectors. In the case study involving the therapeutic rehabilitat…

View free PDFSource page
crossrefApplied Sciences2026-02-28

Comparative Analysis of Machine Learning and Deep Learning Models for Atrial Fibrillation Detection from Long-Term ECG

Lerina Aversano, Ilaria Mancino, Agostino Marengo, Chiara Verdone

Atrial fibrillation is the most prevalent sustained cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature mortality. Automatic detection remains challenging due to the variability of electrocardiogram (ECG) morphology, noise, and the paroxysmal natur…

View free PDFSource page
crossrefApplied Sciences2026-01-03

Towards Intelligent Manufacturing: Machine Learning, Deep Learning, and Computer Vision for Tool Wear Estimation in Milling and Micromilling Processes

Vaibhav Joshi, Sameer Sayyad, Arunkumar Bongale, Satish Kumar, Vivek Warke, R. Suresh

In modern manufacturing, milling and micromilling processes play a central role in precision production. However, rapid wear of cutting tools often leads to sudden tool breakage, unplanned downtime, and part rejection. Maintenance is therefore essential to ensure efficiency, safe…

View free PDFSource page
crossrefApplied Sciences2025-01-21Cited by 7

Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision

Junzhe Jiang, Rongnian Tang, Weian Kang, Zengcai Xu, Cheng Qian

One of the basic requirements of an automated parking system is to quickly and accurately detect parking spaces. With the development of deep convolutional neural networks, parking space detection systems are becoming increasingly mature. However, the existing detection systems a…

View free PDFSource page