Browsing by Author "Engelbrecht, Jarrett"

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    Identification of driving manoeuvres using smartphone-based GPS and inertial forces measurement
    (Stellenbosch : Stellenbosch University, 2015-03) Engelbrecht, Jarrett; Booysen, M. J.; Van Rooyen, G-J.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Road accidents are a growing concern for governments and is rising to become one of the leading causes of death in developing countries. Aggressive driving is one of the major causes of road accidents, and it is therefore important to investigate ways to improve people's driving habits. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks in vehicles, and therefore this thesis focuses on the use of smartphones to monitor a person's driving behaviour. The framework for a smartphone-based system that can detect and classify various driving manoeuvres is researched. As a proof of concept, a system is developed that specifically detects lateral driving manoeuvres and that classifies them as aggressive or not, using a supervised learning classification algorithm. Existing solutions found in research literature are investigated and presented. The best existing solution, a dynamic time warping classification approach, is also implemented and tested. We use an aggressive driving model that is based on the angle of a turn, the lateral force exerted on the vehicle and its speed through the turn. The tests and results of the implemented manoeuvre detection and classifcation algorithms are presented, and thoroughly discussed. The performance of each classifer is tested using the same data set, and a quantitative comparison are made between them. Ultimately, a lateral driving manoeuvre detection and recognition system was successfully developed, and its potential to be implemented on a smartphone was substantiated. The suitability of supervised learning classi ers for classifying aggressive driving, in comparison to dynamic time warping classifcation, was successfully demonstrated and used to validate our aggressive driving model. Conceivably, this work can be employed in the future to develop an holistic smartphone-based driver behaviour monitoring system, which can be easily deployed on a large scale to help make the public drive better. This would make our roads safer, reducing the occurrence of road accidents and fatalities.
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    Recognition of driving manoeuvres using smartphone-based inertial and GPS measurement
    (2014-12) Engelbrecht, Jarrett; Booysen, Marthinus J.; Van Rooyen, Gert-Jan
    The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks for ITS applications. In this paper we show how the embedded sensors and GPS of a smartphone can be used to recognize driving manoeuvres. Smartphone-based driving behaviour monitoring has applications in the insurance industry and for law enforcement. The proposed solution is suitable for real-time applications, such as driver assistance and safety systems. An endpoint detection algorithm is used on filtered accelerometer and gyroscope data to find the start- and endpoints of driving events. The relevant sensor data is compared against different sets of manoeuvre signal templates using the dynamic time warping (DTW) algorithm. A heuristic method is then used to classify a manoeuvre as normal or aggressive based on its speed and closest matching acceleration and rotation rate templates.