Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Driver identification using 1D convolutional neural networks with vehicular CAN signals

  • Author(s): Hongyu Hu 1, 2 ; Jiarui Liu 3 ; Zhenhai Gao 1 ; Pin Wang 2
    • View affiliations
  • Source: Volume 14, Issue 13, 15 December 2020, p. 1799 – 1809
    DOI:  10.1049/iet-its.2020.0105 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 21/02/2020, Accepted 09/11/2020, Revised 16/10/2020, Published 26/11/2020

This study proposes a deep learning framework for driver identity identification by extracting information from the vehicular controller area network (CAN) bus signals. First, naturalistic driving data of 20 drivers were collected under a fixed testing route with different road types and different traffic conditions. Then, a one-dimensional convolutional neural network was constructed for driver identification, which consists of two convolutional-pooling layers, a fully connected layer, and a SoftMax layer. Model optimisation algorithms were applied to improve accuracy and speed up the training process. Also, the model parameters were optimised by evaluating their influences on the model results. Furthermore, the performance of the proposed algorithm was compared with that of the K-nearest neighbour, support vector machine, multi-layer perceptron, and long short-term memory model. The authors used the score as an evaluation criterion and the identification score of the authors' proposed model reaches 99.10% under 20 testing subjects where the data time window size is one second and the sample data overlap is 80%. The results show that the model's performance is significantly better than the other algorithms, which can effectively identify driver identities with stability and robustness.

Inspec keywords: convolutional neural nets; nearest neighbour methods; support vector machines; controller area networks; multilayer perceptrons; road safety; driver information systems; learning (artificial intelligence); optimisation

Other keywords: identification score; convolution filters; 1D convolutional neural networks; convolutional-pooling layers; fully connected layer nodes; fixed testing route; one-dimensional convolutional neural network; vehicular CAN signals; vehicular controller area network bus signals; deep learning framework; driver identification; K-nearest neighbour; road types; driver identity identification; model parameters; multilayer perceptron; long short-term memory model; sample data overlap; naturalistic driving data; model optimisation algorithms; SoftMax layer; data time window size

Subjects: Traffic engineering computing; Neural computing techniques; Computer vision and image processing techniques; Knowledge engineering techniques; Optimisation techniques

References

    1. 1)
      • 5. Hallac, D., Sharang, A., Stahlmann, R., et al: ‘Driver identification using automobile sensor data from a single turn’. 19th Int. IEEE Conf. on Intelligent Transportation Systems, Rio de Janeiro, Brazil, November 2016, pp. 953–958.
    2. 2)
      • 19. Campo, D.I.., Finker, R., Martinez, M. V., et al: ‘A real-time driver identification system based on artificial neural networks and cepstral analysis’. 2014 IEEE Int. Joint. Con. on Neural Networks, Beijing, China, July 2014, pp. 1848–1855.
    3. 3)
      • 2. Qi, G., Du, Y., Wu, J., et al: ‘What is the appropriate temporal distance range for driving style analysis?’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (5), pp. 1393–1403.
    4. 4)
      • 21. Lecun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436–444.
    5. 5)
      • 26. Gao, F., Wang, C.: ‘Hybrid strategy for traffic light detection by combining classical and self-learning detectors’, IET Intell. Transp. Syst., 2020, 14, (7), pp. 735–741.
    6. 6)
      • 40. Enev, M., Takakuwa, A., Koscher, K., et al: ‘Automobile driver fingerprinting’. Proc. on Privacy Enhancing Technologies, Darmstadt, Germany, July 2016, pp. 34-50.
    7. 7)
      • 31. Krizhevsky, A., Sutskever, I., Hinton, G.: ‘Imagenet classification with deep convolutional neural networks’. Int. Conf. Advance in Neural. Information Processing System, Lake Tahoe, Nevada, USA, December 2012, pp. 1097–1105.
    8. 8)
      • 16. Luo, D., Lu, J., Guo, G.: ‘Driver identification using multivariate in-vehicle time series data’. SAE Technical, Detroit, Michigan, USA, April, 2018, pp. 01–1198, http://americajr.com/news/category/sports/detroit-sports-sports/.
    9. 9)
      • 3. Fridman, L.: ‘Human-centered autonomous vehicle systems: principles of effective shared autonomy’, arXiv preprint arXiv: 1810.01835, 2018.
    10. 10)
      • 32. Kiranyaz, S., Ince, T., Gabbouj, M.: ‘Real-time patient-specific ECG classification by 1D convolutional neural networks’, IEEE Trans. Biomed. Eng., 2015, 63, (3), pp. 664–675.
    11. 11)
      • 30. LeCun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 2278–2324.
    12. 12)
      • 25. Leung, M.K., Xiong, H.Y., Lee, L.J., et al: ‘Deep learning of the tissue-regulated splicing code’, Bioinformatics, 2014, 30, (12), pp. 121–129.
    13. 13)
      • 23. Hinton, G., Deng, L., Yu, D., et al: ‘Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups’, IEEE Signal Process. Mag., 2012, 29, (6), pp. 82–97.
    14. 14)
      • 34. Kingma, D.P., Ba, J.: ‘Adam: a method for stochastic optimization’, arXiv preprint, arXiv: 1412.6980, 2014.
    15. 15)
      • 7. Marchegiani, L., Posner, I.: ‘Long-term driving behaviour modelling for driver identification’. 21st Int. Conf. on Intelligent Transportation Systems, Maui, HI, November 2018, pp. 913–919.
    16. 16)
      • 28. Li, L., Lv, Y., Wang, F.Y.: ‘Traffic signal timing via deep reinforcement learning’, IEEE/CAA J. Autom. Sin., 2016, 3, (3), pp. 247–254.
    17. 17)
      • 15. Jeong, D., Kim, M.S., Kim, K.T., et al: ‘Real-time driver identification using vehicular big data and deep learning’. 2018 IEEE Int. Conf. on Intelligent Transportation Systems (ITSC), Maui, HI, November 2018, pp. 123–130.
    18. 18)
      • 10. Juan, C., García, F., Martín, D., et al: ‘Data fusion for driver behaviour analysis’, Sensors, 2015, 15, (10), pp. 25968–25991.
    19. 19)
      • 13. Fugiglando, U., Massaro, E., Santi, P., et al: ‘Driving behavior analysis through CAN bus data in an uncontrolled environment’, IEEE Trans. Intell. Transp. Syst., 2019, 20, (2), pp. 737–748.
    20. 20)
      • 11. Hong, J.H., Margines, B., Dey, A.K., ‘A smartphone-based sensing platform to model aggressive driving behaviors’. SIGCHI Con. Human Factors in Computing Systems, Toronto, Ontario, Canada, April 2014, pp. 4047–4056.
    21. 21)
      • 39. Saleh, K., Hossny, M., Nahavandi, S.: ‘Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks’. Int. IEEE Conf. on Intelligent Transportation Systems, Yokohama, Japan, October 2017, pp. 1–6.
    22. 22)
      • 37. Szegedy, C., Vanhoucke, V., Loffe, S., et al: ‘Rethinking the inception architecture for computer vision’. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June 2016, pp. 2818–2826.
    23. 23)
      • 9. Brambilla, M., Mascetti, P., Mauri, A.: ‘Comparison of different driving style analysis approaches based on trip segmentation over GPS information’. 2017 IEEE Int. Conf. on Big Data, Boston, USA, June 2017, pp. 3784–3791.
    24. 24)
      • 20. Virojboonkiate, N., Chanakitkarnchokb, A., Vateekul, P., et al: ‘Public transport driver identification system using histogram of acceleration data’, J. Adv. Transp., 2019, 6372597, pp. 1–15.
    25. 25)
      • 36. Liu, C., Wang, W., Wang, M., et al: ‘An efficient instance selection algorithm to reconstruct training set for support vector machine’, Knowled.-Based Syst., 2017, 116, (1), pp. 58–73.
    26. 26)
      • 12. Kwak, B.I., Woo, J., Kim, H.K.: ‘Know your master: driver profiling-based anti-theft method’. 14th Int. Conf. on Privacy, Security and Trust (PST), Auckland, New Zealand, December 2016, pp. 211–218.
    27. 27)
      • 8. Moreira-Matias, L., Farah, H.: ‘On developing a driver identification methodology using In-vehicle data recorders’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (9), pp. 2387–2396.
    28. 28)
      • 4. Gao, F., He, B., He, Y.: ‘Detection of driving capability degradation for human-machine cooperative driving’, Sensors, 2020, 20, (7), pp. 1–14.
    29. 29)
      • 18. Ezzini, S., Berrada, I., Ghogho, M.: ‘Who is behind the wheel? Driver identification and fingerprinting’, J. Big Data, 2018, 5, (1), pp. 1–9.
    30. 30)
      • 29. Konoplich, G.V., Putin, E.O., Filchenkov, A.A.: ‘Application of deep learning to the problem of vehicle detection in UAV images’. IEEE Int. Conf. on Soft Computing & Measurements, Petersburg, RUS, May 2016, pp. 4–6.
    31. 31)
      • 27. Zhao, Z., Chen, W., Wu, X., et al: ‘LSTM network: a deep learning approach for short-term traffic forecast’, IEEE Trans. Intell. Transp. Syst., 2017, 11, (2), pp. 68–75.
    32. 32)
      • 33. Ruder, S.: ‘An overview of gradient descent optimization algorithms’, arXiv preprint, arXiv: 1609. 04747, 2016.
    33. 33)
      • 35. Srivastava, N., Hinton, G., Krizhevsky, A., et al: ‘Dropout: a simple way to prevent neural networks from overfitting’, J. Mach. Learn. Res., 2014, 15, (1), pp. 1929–1958.
    34. 34)
      • 38. Maaten, L., Hinton, G., ‘Visualizing data using t-SNE’, J. Mach. Learn. Res., 2008, 9, (11), pp. 2579–2605.
    35. 35)
      • 17. Martinez, M.V., Campo, I.D., Echanobe, J., et al: ‘Driving behavior signals and machine learning: a personalized driver assistance system’. 18th Int. Conf. on Intelligent Transportation Systems, Gran, Canaria, Spain, September 2015, pp. 2933–2940.
    36. 36)
      • 22. Farabet, C., Couprie, C., Najman, L., et al: ‘Learning hierarchical features for scene labeling’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (8), pp. 1915–1929.
    37. 37)
      • 1. Butakov, V., Ioannou, P.: ‘Personalized driver/vehicle lane change models for ADAS’, IEEE Trans. Veh. Technol., 2015, 64, (10), pp. 4422–4431.
    38. 38)
      • 14. Mekki, A.E., Bouhoute, A., Berrada, I., et al: ‘Improving driver identification for the next-generation of in-vehicle software systems’, IEEE Trans. Veh. Technol., 2019, 68, (8), pp. 7406–7415.
    39. 39)
      • 24. Sainath, T.N., Mohamed, A.R., Kingsbury, B., et al: ‘Deep convolutional neural networks for LVCSR’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vancouver, BC, Canda, May 2013, pp. 8614–8618.
    40. 40)
      • 6. Martinez, C.M., Heucke, M., Wang, F.Y., et al: ‘Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey’, IEEE Trans. Intell. Transp. Syst., 2017, 19, (3), pp. 666–6676.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2020.0105
Loading

Related content

content/journals/10.1049/iet-its.2020.0105
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address