Leveraging Smartphone Sensors to Detect Distracted Driving Activities
- 0 Collaborators
In this paper, we explore the feasibility of leveraging the accelerometer and gyroscope sensors in modern smartphones to detect instances of distracting driving activities (e.g., calling, texting and reading while driving). To do so, we conducted an experiment with 16 subjects on a realistic driving simulator. As discussed later, the simulator is equipped with a realistic steering wheel, acceleration/ braking pedals, and a wide screen to visualize background vehicular traffic. It is also programmed to simulate multiple environmental conditions like day time, night time, fog and rain/ snow. Subjects were instructed to drive the simulator while performing a randomized sequence of activities that included texting, calling and reading from a phone while they were driving, during which the accelerometer and gyroscope in the phone were logging sensory data. By extracting features from this sensory data, we then implemented a machine learning technique based on Random Forests to detect distracted driving. Our technique achieves very good Precision, Recall and FMeasure across all environmental conditions we tested. We believe that our contributions in this paper can have a significant impact on enhancing road safety ...learn more
Project status: Published/In Market
Internet of Things, Artificial Intelligence
Groups
Student Developers for AI
Overview / Usage
In this paper, we explore the feasibility of leveraging the accelerometer and gyroscope sensors in modern smartphones to detect instances of distracting driving activities (e.g., calling, texting and reading while driving). To do so, we conducted an experiment with 16 subjects on a realistic driving simulator. As discussed later, the simulator is equipped with a realistic steering wheel, acceleration/ braking pedals, and a wide-screen to visualize background vehicular traffic. It is also programmed to simulate multiple environmental conditions like daytime, nighttime, fog and rain/ snow. Subjects were instructed to drive the simulator while performing a randomized sequence of activities that included texting, calling and reading from a phone while they were driving, during which the accelerometer and gyroscope in the phone were logging sensory data. By extracting features from this sensory data, we then implemented a machine learning technique based on Random Forests to detect distracted driving. Our technique achieves very good Precision, Recall and FMeasure across all environmental conditions we tested. We believe that our contributions in this paper can have a significant impact on enhancing road safety
Methodology / Approach
Feature selection using information gain, Machine Learning, Random Forest.
Technologies Used
Python