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| Title | Driver Inattentiveness Detection Techniques For Intelligent Transportation Systems: A Review |
|---|---|
| ID_Doc | 21018 |
| Authors | Patel A.; Chhabra R.; Krishna C.R. |
| Year | 2023 |
| Published | 2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023 - Proceedings |
| DOI | http://dx.doi.org/10.1109/DELCON57910.2023.10127504 |
| Abstract | Due to technical advancement, Intelligent Transportation System (ITS) aims to maximize driver safety and security. ITS helps us increase the safety and convenience of the overall transportation system. It aims to incorporate new technology into an already existing traditional transportation system to create a more efficient traffic system that both drivers and others in-charge of managing the traffic can use conveniently. ITS plays a crucial role in development of future smart cities. The core of any transportation system is its drivers. Multiple factors, including distracted driving while using a smartphone, driving while intoxicated, driving while talking on the phone, and many more, have dramatically increased the number of traffic accidents. Driver fatigue is another factor that negatively affects driving attention. Hence, detecting the driver's inattentiveness is an integral part of the ITS as it heavily ensures the safety of both drivers and passengers on the road. In this paper, we present a survey of various driver inattentiveness detection techniques using IoT, Machine Learning, or Deep Learning detection techniques. We initially require input before we can implement any of the detection strategies. Specific wearables, bio-signal sensors, cameras, and smartphone sensors, which include the magnetometer, gyroscope, GPS, and accelerometer, which are embedded into a smartphone, can be used to collect the required input. A comparative analysis has been carried out based on the benefits, drawbacks, and methods used in various techniques. Furthermore, new research directions for driver inattentiveness detection on the road have been discussed. © 2023 IEEE. |
| Author Keywords | deep learning; driver inattentiveness; ITS; machine learning; sensors |
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