AI Medical Compendium Journal:
Traffic injury prevention

Showing 1 to 10 of 18 articles

Comparison of deep learning approaches to estimate injury severity from the International Classification of Diseases codes.

Traffic injury prevention
OBJECTIVE: The injury severity classification based on the Abbreviated Injury Scale (AIS) provides information that allows for standardized comparisons for injury research. However, the majority of injury data is captured using the International Clas...

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...

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.

Traffic injury prevention
OBJECTIVE: In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety.

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.

Recognition of aggressive driving behavior under abnormal weather based on Convolutional Neural Network and transfer learning.

Traffic injury prevention
OBJECTIVES: Aggressive driving behavior can lead to potential traffic collision risks, and abnormal weather conditions can exacerbate this behavior. This study aims to develop recognition models for aggressive driving under various climate conditions...

Child face detection on front passenger seat through deep learning.

Traffic injury prevention
OBJECTIVE: One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the a...

Investigating mental workload caused by NDRTs in highly automated driving with deep learning.

Traffic injury prevention
OBJECTIVE: This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) sig...

Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies.

Traffic injury prevention
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...

The driver's instantaneous situation awareness when the alarm rings during the take-over of vehicle control in automated driving.

Traffic injury prevention
OBJECTIVE: The driver's instantaneous situation awareness in the process of take-over of vehicle control in automated driving has not yet been thoroughly investigated. The proposed research can provide a better understanding of the driver's perceived...