AIMC Topic: Accidents, Traffic

Clear Filters Showing 111 to 120 of 307 articles

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry.

BMC medical informatics and decision making
BACKGROUND: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient's condition and deciding on transport destination. Data-driven On Scene Injur...

A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns.

Journal of research in health sciences
BACKGROUND: Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians'...

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...