Analysis and Development of a Novel Algorithm for the In-vehicle Hand-Usage of a Smartphone (original) (raw)
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Proceeding of the 11th annual international conference on Mobile systems, applications, and services, 2013
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Cornell University - arXiv, 2021
Traffic-related injuries and fatalities are major health risks in the United States. Mobile phone use while driving quadruples the risk for a motor vehicle crash. This work demonstrates the feasibility of using the mobile phone camera to passively detect the location of the phone's user within a vehicle. In a large, varied dataset we were able correctly identify if the user was in the driver's seat or one of the passenger seats with 94.9% accuracy. This model could be used by application developers to selectively change or lock functionality while a user is driving, but not if the user is a passenger in a moving vehicle.
Driving behavior analysis with smartphones
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia - MUM '12, 2012
We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.