Road type classification through data mining

Authors:
Phillip Taylor;Sarabjot Singh Anand;Nathan Griffiths;Fatimah Adamu-Fika;Alain Dunoyer;Thomas Popham
Affiliations:
University of Warwick, UK;University of Warwick, UK;University of Warwick, UK;University of Warwick, UK;Jaguar Land Rover, UK;Jaguar Land Rover, UK
Venue:
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Year:
2012

Citing 5
Cited 0

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning from Imbalanced Data

IEEE Transactions on Knowledge and Data Engineering
Estimation of driver's fatigue based on steering wheel angle

EPCE'11 Proceedings of the 9th international conference on Engineering psychology and cognitive ergonomics
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

IEEE Transactions on Intelligent Transportation Systems
Automatic Road Environment Classification

IEEE Transactions on Intelligent Transportation Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we investigate data mining approaches to road type classification based on CAN (controller area network) bus data collected from vehicles on UK roads. We consider three related classification problems: road type (A, B, C and Motorway), signage (None, White, Green and Blue) and carriageway type (Single or Double). Knowledge of these classifications has a number of uses, including tuning the engine and adapting the user interface according to the situation. Furthermore, the current road type and surrounding area gives an indication of the driver's workload. In a residential area the driver is likely to be overloaded, while they may be under stimulated on a highway. Several data mining and temporal analysis techniques are investigated, along with selected ensemble classifiers and initial attempts to deal with a class imbalance present in the data. We find that the Random Forest ensemble algorithm has the best performance, with an AUC of 0.89 when used with a wavelet-Gaussian summary of the previous 2.5 seconds of speed and steering wheel angle recordings. We show that this technique is at least as good as a model-based solution that was manually created using domain expertise.