Learning Driver Braking Behavior using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study

Stavros Christopoulos, Stratis Kanarachos, Alexander Chroneos

    Research output: Contribution to journalArticlepeer-review

    29 Citations (Scopus)
    364 Downloads (Pure)

    Abstract

    The present study focuses on the automated learning of driver braking “signature” in the presence of road anomalies. Our motivation is to improve driver experience using preview information from navigation maps. Smartphones facilitate, due to their unprecedented market penetration, the large-scale deployment of Advanced Driver Assistance Systems (ADAS). On the other hand, it is challenging to exploit smartphone sensor data because of the fewer and lower quality signals, compared to the ones on board. Methods for detecting braking behavior using smartphones exist, however, most of them focus only on harsh events. Additionally, only a few studies correlate longitudinal driving behavior with the road condition. In this paper, a new method, based on Deep Neural Networks and the sliding correlation coefficient, is proposed for the spatio-temporal correlation of road anomalies and driver behavior. A unique Deep Neural Network structure, that requires minimum tuning, is proposed. Extensive field trials were conducted and vehicle motion was recorded using smartphones and a data acquisition system, comprising an IMU and differential GPS. The proposed method was validated using the probabilistic Receiver Operating Characteristics method. The method proves to be a robust and flexible tool for self-learning driver behavior.
    Original languageEnglish
    Pages (from-to)65-74
    Number of pages10
    JournalIEEE Transactions on Intelligent Transportation Systems
    Volume20
    Issue number1
    Early online date16 Feb 2018
    DOIs
    Publication statusPublished - Jan 2019

    Bibliographical note

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Keywords

    • Smart phones
    • Roads
    • Vehicles
    • Acceleration
    • Global Positioning System
    • Accelerometers
    • Correlation
    • Advanced driver assistance systems
    • braking behavior
    • neural networks
    • smartphones
    • road condition

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