Priyanka Sundararajan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Priyanka Sundararajan has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Abilash Kasi
Christ University, Bangalore, India
2 shared publications - Kimaya Gokul Dafale
Christ University, India
2 shared publications - Nithina Odayammadathu Malethu Veedu
Christ University, India
2 shared publications
Latest Publications
- 3 July 2026
- Published by IGI Global Scientific Publishing
- p. 2653-2672
Eyes on the Road
Fatigue is a major contributing factor to accidents involving professional drivers. Traditional methods of detecting fatigue such as self-reported questionnaires or observation are often ineffective and impractical. Artificial intelligence offers a promising solution to this problem by providing a real-time, objective, and non-intrusive way to detect fatigue. This review aims to outline AI-based fatigue detection systems that can be implemented for long-haul drivers. It uses technology to identify and track the driver's behavior and physiological indicators to detect signs of fatigue. Integrating an AI-based fatigue detection system into the vehicle's dashboard could provide real-time alerts when the drivers are drowsy as well as provide data to fleet managers to target interventions to drivers at risk of drowsy driving. This kind of fatigue detection system is relatively inexpensive to implement and can reduce the number of accidents caused by drowsy driving significantly.
- 3 July 2026
- Published by IGI Global Scientific Publishing
- p. 2653-2672
Eyes on the Road
Fatigue is a major contributing factor to accidents involving professional drivers. Traditional methods of detecting fatigue such as self-reported questionnaires or observation are often ineffective and impractical. Artificial intelligence offers a promising solution to this problem by providing a real-time, objective, and non-intrusive way to detect fatigue. This review aims to outline AI-based fatigue detection systems that can be implemented for long-haul drivers. It uses technology to identify and track the driver's behavior and physiological indicators to detect signs of fatigue. Integrating an AI-based fatigue detection system into the vehicle's dashboard could provide real-time alerts when the drivers are drowsy as well as provide data to fleet managers to target interventions to drivers at risk of drowsy driving. This kind of fatigue detection system is relatively inexpensive to implement and can reduce the number of accidents caused by drowsy driving significantly.
- 19 June 2026
- Published by IGI Global Scientific Publishing
- p. 2653-2672
Eyes on the Road
Fatigue is a major contributing factor to accidents involving professional drivers. Traditional methods of detecting fatigue such as self-reported questionnaires or observation are often ineffective and impractical. Artificial intelligence offers a promising solution to this problem by providing a real-time, objective, and non-intrusive way to detect fatigue. This review aims to outline AI-based fatigue detection systems that can be implemented for long-haul drivers. It uses technology to identify and track the driver's behavior and physiological indicators to detect signs of fatigue. Integrating an AI-based fatigue detection system into the vehicle's dashboard could provide real-time alerts when the drivers are drowsy as well as provide data to fleet managers to target interventions to drivers at risk of drowsy driving. This kind of fatigue detection system is relatively inexpensive to implement and can reduce the number of accidents caused by drowsy driving significantly.
- 19 June 2026
- Published by IGI Global Scientific Publishing
- p. 2653-2672
Eyes on the Road
Fatigue is a major contributing factor to accidents involving professional drivers. Traditional methods of detecting fatigue such as self-reported questionnaires or observation are often ineffective and impractical. Artificial intelligence offers a promising solution to this problem by providing a real-time, objective, and non-intrusive way to detect fatigue. This review aims to outline AI-based fatigue detection systems that can be implemented for long-haul drivers. It uses technology to identify and track the driver's behavior and physiological indicators to detect signs of fatigue. Integrating an AI-based fatigue detection system into the vehicle's dashboard could provide real-time alerts when the drivers are drowsy as well as provide data to fleet managers to target interventions to drivers at risk of drowsy driving. This kind of fatigue detection system is relatively inexpensive to implement and can reduce the number of accidents caused by drowsy driving significantly.