Distributed road surface condition monitoring using mobile phones

Authors:
Mikko Perttunen;Oleksiy Mazhelis;Fengyu Cong;Mikko Kauppila;Teemu Leppänen;Jouni Kantola;Jussi Collin;Susanna Pirttikangas;Janne Haverinen;Tapani Ristaniemi;Jukka Riekki
Affiliations:
Computer Science and Engineering Laboratory, University of Oulu, Oulu, Finland;Faculty of Information Technology, University of Jyväskylä, Jyvaskyla, Finland;Faculty of Information Technology, University of Jyväskylä, Jyvaskyla, Finland;Computer Science and Engineering Laboratory, University of Oulu, Oulu, Finland;Computer Science and Engineering Laboratory, University of Oulu, Oulu, Finland;Department of Computer Systems, Tampere University of Technology, Tampere, Finland;Department of Computer Systems, Tampere University of Technology, Tampere, Finland;Computer Science and Engineering Laboratory, University of Oulu, Oulu, Finland;Computer Science and Engineering Laboratory, University of Oulu, Oulu, Finland;Faculty of Information Technology, University of Jyväskylä, Jyvaskyla, Finland;Computer Science and Engineering Laboratory, University of Oulu, Oulu, Finland
Venue:
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Year:
2011

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Abstract

The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.