AIMC Topic: Accidents, Traffic
Clear Filters Showing 101 to 110 of 284 articles
Cycle-level traffic conflict prediction at signalized intersections with LiDAR data and Bayesian deep learning.
Accident; analysis and prevention
Aug 29, 2023
Real-time safety prediction models are vital in proactive road safety management strategies. This study develops models to predict traffic conflicts at signalized intersections at the signal cycle level, using advanced Bayesian deep learning techniqu...
PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference.
Accident; analysis and prevention
Jul 5, 2023
A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human b...
Data generation for connected and automated vehicle tests using deep learning models.
Accident; analysis and prevention
Jun 26, 2023
For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited b...
DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning.
Sensors (Basel, Switzerland)
Jun 18, 2023
Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT dev...
Road Feature Detection for Advance Driver Assistance System Using Deep Learning.
Sensors (Basel, Switzerland)
May 4, 2023
Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approach...
Older driver at-fault crashes at unsignalized intersections in Alabama: Injury severity analysis with supporting evidence from a deep learning based approach.
Journal of safety research
Apr 27, 2023
INTRODUCTION: The research described in this paper explored the factors contributing to the injury severity resulting from the male and female older driver (65 years and older) at-fault crashes at unsignalized intersections in Alabama.
Investigating the effect of road condition and vacation on crash severity using machine learning algorithms.
International journal of injury control and safety promotion
Apr 20, 2023
Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial an...
The usefulness of artificial intelligence for safety assessment of different transport modes.
Accident; analysis and prevention
Mar 28, 2023
Recent research in transport safety focuses on the processing of large amounts of available data by means of intelligent systems, in order to decrease the number of accidents for transportation users. Several Machine Learning (ML) and Artificial Inte...
Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies.
Traffic injury prevention
Feb 14, 2023
OBJECTIVE: The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomo...
Deep Learning with Attention Mechanisms for Road Weather Detection.
Sensors (Basel, Switzerland)
Jan 10, 2023
There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assi...
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