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Accidents, Traffic

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Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach.

Sensors (Basel, Switzerland)
A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A ...

SWADAPT2: benefits of a collision avoidance assistance for powered wheelchair users in driving difficulty.

Disability and rehabilitation. Assistive technology
PURPOSE: In France, tens of thousands of people use a wheelchair. Driving powered wheelchairs (PWCs) present risks for users and their families. The risk of collision in PWC driver increases with severity of disability and may reduce their independen...

A framework for proactive safety evaluation of intersection using surrogate safety measures and non-compliance behavior.

Accident; analysis and prevention
In recent years, identifying road users' behavior and conflicts at intersections have become an essential data source for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, more than 50% of fatal and injury cr...

Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach.

Accident; analysis and prevention
For each road crash event, it is necessary to predict its injury severity. However, predicting crash injury severity with the imbalanced data frequently results in ineffective classifier. Due to the rarity of severe injuries in road traffic crashes, ...

Cycle-level traffic conflict prediction at signalized intersections with LiDAR data and Bayesian deep learning.

Accident; analysis and prevention
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
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
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)
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)
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
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.