Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention
Abstract
This paper presents a methodology and mobile application for driver monitoring, analysis, and recommendations based on detected unsafe driving behavior for accident prevention using a personal smartphone. For the driver behavior monitoring, the smartphone's cameras and built-in sensors (accelerometer, gyroscope, GPS, and microphone) are used. A developed methodology includes dangerous state classification, dangerous state detection, and a reference model. The methodology supports the following driver's online dangerous states: distraction and drowsiness as well as an offline dangerous state related to a high pulse rate. We implemented the system for Android smartphones and evaluated it with ten volunteers.
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Estimation of human pose and recognition of action can be very useful in many real-world AIoT scenarios, such as rehabilitation exercises monitoring and assessment, dangerous behavior monitoring, and human-machine interaction.
“Estimation of human pose and recognition of action can be very useful in many real-world AIoT scenarios, such as rehabilitation exercises monitoring and assessment [120], dangerous behavior monitoring [121], and human-machine interaction (HMI).”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [121]
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- Authors
- Kashevnik, A., Lashkov, I., & Gurtov, A.
- Affiliations
- Russian Academy of Sciences (RU)
- Linköping University (SE)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-06
- Volume
- 21
- Issue
- 6
- Pages
- 2427-2436
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2948780138
- Cited by
- 139
- References
- 31
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 15 · 2023: 33 · 2022: 29 · 2021: 30 · 2020: 14 · 2019: 1
- FWCI
- 23.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Russian Science Foundation (18-71-10065)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Keywords
- driver behavior monitoring, accident prevention, dangerous driving detection, driver distraction, driver drowsiness, smartphone sensors, pulse rate monitoring, Android application
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