AIMC Topic: Automobile Driving

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Identification of the best machine learning model for the prediction of driver injury severity.

International journal of injury control and safety promotion
Predicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. Thes...

Lane-change intention recognition considering oncoming traffic: Novel insights revealed by advances in deep learning.

Accident; analysis and prevention
Lane-changing (LC) intention recognition models have seen limited real-world application due to a lack of research on two-lane two-way road environments. This study constructs a high-fidelity simulated two-lane two-way road to develop a Transformer m...

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...

A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data.

Accident; analysis and prevention
On freeways, sudden deceleration or lane-changing by vehicles can trigger conflict risk that propagates backward in a specific pattern. Simulating this pattern of conflict risk propagation can not only help prevent crashes but is also vital for the d...

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 ...

The impact of co-pilot displays use on driver workload and driving performance exploring the impact of co-pilot display on drivers' workload and driving performance.

Applied ergonomics
New production vehicles equipped with multiple screens have various functions and technologies at the user's fingertips that create informative and immersive cockpits not only for the driver but also for the passenger. Despite the growing popularity ...

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...

Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.

Sensors (Basel, Switzerland)
Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has ...

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...

Dense reinforcement learning for safety validation of autonomous vehicles.

Nature
One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critic...