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Automobile Driving

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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 ...

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 ...

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...

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...

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...

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.

Sensors (Basel, Switzerland)
Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus h...

Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning.

Accident; analysis and prevention
To facilitate efficient transportation, I-4 Express is constructed separately from general use lanes in metropolitan area to improve mobility and reduce congestion. As this new infrastructure would undoubtedly change the traffic network, there is a n...

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...

Cognitive workload classification of law enforcement officers using physiological responses.

Applied ergonomics
Motor vehicle crashes (MVCs) are a leading cause of death for law enforcement officers (LEOs) in the U.S. LEOs and more specifically novice LEOs (nLEOs) are susceptible to high cognitive workload while driving which can lead to fatal MVCs. The object...

Acceptance and Usability of a Soft Robotic, Haptic Feedback Seat for Autonomy Level Transitions in Highly Automated Vehicles.

IEEE transactions on haptics
Fully autonomous vehicles, capable of completing entire end-to-end journeys without the interference of a human driver, will be one of the biggest transforming technologies of the next decades. As the journey towards fully autonomous vehicles progres...