AIMC Topic: Safety

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An integrative approach to generating explainable safety assessment scenarios for autonomous vehicles based on Vision Transformer and SHAP.

Accident; analysis and prevention
Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insi...

Survey evidence on public support for AI safety oversight.

Scientific reports
A number of AI safety concerns are being increasingly discussed by experts, including misinformation, invasion of privacy, job displacement, and criminal misuse. Two exploratory studies conducted in Germany and Spain (combined n = 2864) provide evide...

Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections.

Accident; analysis and prevention
Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This rese...

Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation.

Sensors (Basel, Switzerland)
Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and...

Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision.

Accident; analysis and prevention
Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and ...

Predicting pedestrian-vehicle interaction severity at unsignalized intersections.

Traffic injury prevention
OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptron...

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.

Traffic injury prevention
OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe...

Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework.

Traffic injury prevention
OBJECTIVE: This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.

Optimizing vehicle Front-End structure for e-bike rider Safety: An advanced Multi-Objective approach using injury prediction models.

Accident; analysis and prevention
A multi-objective optimization method based on an injury prediction model is proposed to address the increasingly prominent safety issues for e-bike riders in Chinese road traffic. This method aims to enhance the protective effect of vehicle front-en...

Gauging road safety advances using a hybrid EWM-PROMETHEE II-DBSCAN model with machine learning.

Frontiers in public health
INTRODUCTION: Enhancing road safety conditions alleviates socioeconomic hazards from traffic accidents and promotes public health. Monitoring progress and recalibrating measures are indispensable in this effort. A systematic and scientific decision-m...