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Driver Profiling and Identification Based on Time Series Analysis

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International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

As more and more cars are connected to the internet, the threat of cyber-attacks and illegal access to one’s automobile increases expeditiously. Illegal access to automobiles via. the car’s integrated network system is quite a common phenomenon nowadays. Thus, it is essential to identify drivers on the basis of their driving patterns. Henceforth, this paper presents a driver profiling and identification method based on data acquired from car sensors. In-vehicle sensors generate dozens of operational data streams, and identifying the right representative features for driver profiling is a challenging task. Therefore, in our work to capture human driving behavior dynamics, we have designed a framework based on Long Short Term Memory. Moreover, for extracting relevant and independent features from the Controller Area Network (CAN) dataset, we suggested using feature selection algorithms. The proposed framework is evaluated on the publicly available vehicle CAN OBD-II dataset. While we demonstrate the effectiveness of the proposed architecture, an essential objective of this study is to verify that inter-driver heterogeneity and intra-driver homogeneity can be modeled using time series dependency.

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Availability of Data and Materials

Publicly available in vehicle CAN OBD-II dataset is used in this work.

Abbreviations

ADP:

Automatic Driver Profiling

ADAS:

Advanced Driver Assistance System

NCRB:

National Crime Records Bureau

SOTA:

State-of-the-art results

CAN OBD:

Controller Area Network On Board Diagnostics

KNN:

K-Nearest Neighbour

HMM:

Hidden Markov Model

DBA:

Driving Behaviour Analysis

MLP:

Multilayer Perceptron

RNN:

Recurrent Neural Networks

GPS:

Global Positioning System

GMM:

Gaussian Mixture Model

mRMR:

Maximum Relevance Minimum Redundancy

LSTM:

Long Short Term Memory

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Acknowledgements

The authors wish to thank International Institute of Information Technology Naya Raipur for providing the technical support for carrying out this project.

Funding

No funding is provided by any external agency.

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Author notes

  1. Avantika Singh, Vipulesh Tiwari and Srinivasa KG These authors contributed equally to this work.

Authors and Affiliations

  1. Dr. Shyama Prasad Mukherjee International Institute of Information Technology, Naya Raipur, Raipur, 493661, Chhattisgarh, India

    Avantika Singh, Vipulesh Tiwari & Srinivasa KG

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  1. Avantika Singh
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  2. Vipulesh Tiwari
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  3. Srinivasa KG
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Contributions

VT has formally analyze the problem and developed the source code. AS has conceptualized the methodology, supervised the project and finalized the manuscript writing. SKG has supervised the project. All authors read and approved the final manuscript.

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Correspondence to Avantika Singh.

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Singh, A., Tiwari, V. & KG, S. Driver Profiling and Identification Based on Time Series Analysis. Int. J. ITS Res. 22, 363–373 (2024). https://doi.org/10.1007/s13177-024-00404-5

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