Driving context classification using pattern recognition

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/243371
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Type: Examensarbete för masterexamen
Master Thesis
Title: Driving context classification using pattern recognition
Authors: Henriksson, Mattias
Abstract: The performance of a vehicle system is to a large extent dependent on the driving context, such as the road infrastructure, in which the vehicle is operating. In order to achieve improved performance, di erent vehicle system applications may need to take driving context parameters into account. In this thesis, we develop a pattern recognition framework that classi es driving context based on data recorded by vehicles (speed, steering wheel angle, etc.) in a naturalistic setting. We train the framework on a large data set of vehicle data annotated with map attributes from a map database representing driving context. An inventory is made on the map attributes, nding two kinds of global-scale driving context classes to classify: (1) whether a vehicle is driving in a city or not, and (2) the functional class of the road the vehicle is driving on. We then review four pattern recognition models: Logistic Regression, SVM, Hidden Markov Model, and a simple Baseline model, comparing their ability to classify (1) and (2). We nd that all models reach similar overall prediction accuracies, ranging between 76 % - 80 % for classi cation task (1) and 84 % - 86 % for task (2), but that the models di er slightly with respect to per-class prediction accuracy.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2016
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/243371
Collection:Examensarbeten för masterexamen // Master Theses

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