Driver Distraction Detection and Recognition

Kiran Kumar Chinta, Dr. Fred Barez

Mechanical Engineering Department

San Jose State University

San Jose, CA 95192-0087, USA

Statistics show that the main reason for traffic accidents is human error. Modern vehicles are equipped to protect occupants in the event of a crash. The latest advanced vehicles come with driver behavior monitoring systems in recent years, and many have been proven to be effective systems in the prevention of accidents. However, these systems do not provide a complete solution and can only detect driver fatigue or driver distraction.

It is reported that 2,841 became fatal victims in distraction affected crashes in 2018, and an estimate of 400 thousand individuals was severely wounded in collisions involving drivers who were distracted in the United States in 2018. The report statistics were shown by the National Highway Traffic Safety Administration.

The industry of car manufacturing is struggling to decrease the crashing risks, which involved driver distraction by initiating new driver utilities such as the Blind Spot Detection system and Lane-Departure Warning, also termed as “Advanced Driver Assistance Systems” (ADAS). These sensory-type innovations are embedded in automobiles to sense the surrounding of the drivers to foresee collision.

Recent innovations that can assist in sensing the distraction of drivers and keeping a driver’s concentration on the road could prove useful to improve safety. Depending on the visual modality of the driver, several types of research in the past evaluate the movement of the eye, and using that data; those studies concluded the severity of having a visual distraction from a driver . Like, in one approach, movements of the eyelid of a driver, mouth openings, and head orientations are important foreseer of distraction from a driver . In another approach, distraction is suggested as physical behavior by the precarious list of activities like texting and talking on the cellphone. A blend of body and facial movement data could enhance the accuracy and reliability in observing distracting factors from drivers and thus support the  
improvement of driving utilities.

A significant difference ought to be made between driver fatigue, distraction, and obliviousness They all affect the performance of a driver, and yet they are not brought about by similar factors and affect the behavior in many ways. Fatigue is identified with tiredness and is influencing the drivers because of physical, mental details. Inattention/Obliviousness is termed as anything concerning a person not focused on driving . Fatigue and inattention are  
both considered as driver distraction

This project aims to build an AI model for sensing the distraction of drivers and identifying the kind of distraction depending on the Kinect sensor and Brio camera. For this, the system is divided into three sub-segments; calling arm position (arms up or down, arms right or left), facial expressions (blinking and mouth), and head orientation. Each segment develops important info for gauging the distraction of a driver based on depth mapping of data and color from the Kinect sensor and Brio camera.

Testing is done using a driving simulator and six drivers of different gender, age and nationality for a total of more than six hours of recording. With this experimentation, the result establishes that the developed software successfully works on drivers with eyeglasses, dark skin tone, epicanthal fold. The results have shown that there are fewer accidents and traffic violations with the driver distraction detection system.

Based on experimental work which evaluates the software and results have shown that this method is accurate and might be used in a real car. Compared to existing approaches aiming to detect either inattention or fatigue, this system outputs both with higher-level information, more suitable for context-aware human-machine interaction. Not only can it be used for immediate driver safety, but also for long term statistics about the driver habits.

 


Driver Distraction Detection and Recognition

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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