AIMC Topic: Automobile Driving

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Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.

PloS one
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic infor...

Beyond safety drivers: Applying air traffic control principles to support the deployment of driverless vehicles.

PloS one
By adopting and extending lessons from the air traffic control system, we argue that a nationwide remote monitoring system for driverless vehicles could increase safety dramatically, speed these vehicles' deployment, and provide employment. It is bec...

Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data.

Accident; analysis and prevention
Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will p...

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Accident; analysis and prevention
Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks...

Doppler-Spectrum Feature-Based Human-Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor.

Sensors (Basel, Switzerland)
In this paper, we propose a Doppler-spectrum feature-based human-vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering poi...

Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting.

International journal of environmental research and public health
Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggre...

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM.

Sensors (Basel, Switzerland)
Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to ...

Communication of Hazards in Mixed-Reality Telerobotic Systems: The Usage of Naturalistic Avoidance Cues in Driving Tasks.

Human factors
OBJECTIVE: This study investigates the effect of naturalistic visual cues on human avoidance behavior for a potential use in telerobotic user interfaces incorporating mixed-reality environments (e.g., augmented reality).

Social behavior for autonomous vehicles.

Proceedings of the National Academy of Sciences of the United States of America
Deployment of autonomous vehicles on public roads promises increased efficiency and safety. It requires understanding the intent of human drivers and adapting to their driving styles. Autonomous vehicles must also behave in safe and predictable ways ...

Being watched over by a conversation robot may enhance safety in simulated driving.

Journal of safety research
INTRODUCTION: In an aging society that is more and more information-oriented, being able to replace human passengers' protective effects on vehicle drivers with those of social robots is both essential and promising. However, the effects of a social ...