Abstract
The latest generation cars are often equipped with advanced driver assistance systems, usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver by leveraging several levels of automation. Therefore, it is essential to adapt the ADAS technology to the car driver’s identity to personalize the provided assistance services. For these reasons, such car driver profiling algorithms have been developed by the scientific community. The algorithm herein proposed is able to recognize the driver’s identity with an accuracy close to 99% thanks to ad-hoc specific analysis of the driver’s PhotoPlethysmoGraphic (PPG) signal. In order to rightly identify the driver profile, the proposed approach uses a 1D Dilated Temporal Convolutional Neural Network architecture to learn the features of the collected driver’s PPG signal. The proposed deep architecture is able to correlate the specific PPG features with subject identity enabling the car ADAS services associated with the recognized identity. Extensive validation and testing of the developed pipeline confirmed its reliability and effectiveness.
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Notes
- 1.
STMicroelectronics SPC5 MCUs: https://www.st.com/en/automotive-microcontrollers/spc5-32-bit-automotive-mcus.html.
- 2.
STMicroelectronics ACCORDO 5 Automotive Microcontroller: https://www.st.com/en/automotive-infotainment-and-telematics/automotive-infotainment-socs.html?icmp=tt4379_gl_pron_nov2016.
References
Banna, G.L., et al.: Oral metronomic vinorelbine in advanced non-small cell lung cancer patients unfit for chemotherapy. Anticancer Res. 38(6), 3689–3697 (2018)
Caber, N., Langdon, P.M., Clarkson, P.J.: Intelligent driver profiling system for cars – a basic concept. In: Antona, M., Stephanidis, C. (eds.) UAHCI 2018. LNCS, vol. 10908, pp. 201–213. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92052-8_16
Castignani, G., Frank, R.: SenseFleet: a smartphone-based driver profiling platform. In: 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 144–145. IEEE (2014)
Conoci, S., Rundo, F., Fallica, G., Lena, D., Buraioli, I., Demarchi, D.: Live demonstration of portable systems based on silicon sensors for the monitoring of physiological parameters of driver drowsiness and pulse wave velocity. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–3. IEEE (2018)
Dangra, B.S., Rajput, D., Bedekar, M., Panicker, S.S.: Profiling of automobile drivers using car games. In: 2015 International Conference on Pervasive Computing (ICPC), pp. 1–5. IEEE (2015)
Ferreira, J., Carvalho, E., Ferreira, B.V., de Souza, C., Suhara, Y., Pentland, A., Pessin, G.: Driver behavior profiling: an investigation with different smartphone sensors and machine learning. PLoS ONE 12(4), e0174959 (2017)
Grasso, G., Perconti, P., Plebe, A.: Assessing social driving behavior. In: Karwowski, W., Ahram, T. (eds.) IHSI 2019. AISC, vol. 903, pp. 111–115. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11051-2_17
Grasso, G.M., Lucifora, C., Perconti, P., Plebe, A.: Evaluating mentalization during driving. In: VEHITS, pp. 536–541 (2019)
Kim, K., Choi, H., Jang, B.: Design of the driver-adaptive vehicle interaction system. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 297–299. IEEE (2018)
Mazzillo, M., et al.: Characterization of SiPMs with NIR long-pass interferential and plastic filters. IEEE Photonics J. 10(3), 1–12 (2018)
Mubasher, M.M., Jaffry, S.W., Jahangir, R.: Modeling of individual differences in car-following behaviour of drivers. In: 2017 International Multi-topic Conference (INMIC), pp. 1–7. IEEE (2017)
Rundo, F.: Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Appl. Sci. 9(20), 4460 (2019)
Rundo, F.: Deep LSTM with dynamic time warping processing framework: a novel advanced algorithm with biosensor system for an efficient car-driver recognition. Electronics 9(4), 616 (2020)
Rundo, F., Conoci, S., Ortis, A., Battiato, S.: An advanced bio-inspired photoplethysmography (PPG) and ECG pattern recognition system for medical assessment. Sensors 18(2), 405 (2018)
Rundo, F., Petralia, S., Fallica, G., Conoci, S.: A nonlinear pattern recognition pipeline for PPG/ECG medical assessments. In: Andò, B., et al. (eds.) CNS 2018. LNEE, vol. 539, pp. 473–480. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04324-7_57
Rundo, F., et al.: An innovative deep learning algorithm for drowsiness detection from EEG signal. Computation 7(1), 13 (2019)
Rundo, F., Spampinato, C., Conoci, S.: Ad-hoc shallow neural network to learn hyper filtered photoplethysmographic (PPG) signal for efficient car-driver drowsiness monitoring. Electronics 8(8), 890 (2019)
Rundo, F., Trenta, F., Di Stallo, A.L., Battiato, S.: Advanced Markov-based machine learning framework for making adaptive trading system. Computation 7(1), 4 (2019)
Rundo, F., Trenta, F., di Stallo, A.L., Battiato, S.: Grid trading system robot (GTSbot): a novel mathematical algorithm for trading FX market. Appl. Sci. 9(9), 1796 (2019)
Taeihagh, A., Lim, H.S.M.: Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transp. Rev. 39(1), 103–128 (2019)
Trenta, F., Conoci, S., Rundo, F., Battiato, S.: Advanced motion-tracking system with multi-layers deep learning framework for innovative car-driver drowsiness monitoring. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–5. IEEE (2019)
Vinciguerra, V., et al.: Progresses towards a processing pipeline in photoplethysmogram (PPG) based on SiPMs. In: 2017 European Conference on Circuit Theory and Design (ECCTD), pp. 1–5. IEEE (2017)
Vinciguerra, V., et al.: PPG/ECG Multisite Combo System Based on SiPM Technology. In: Andò, B., et al. (eds.) CNS 2018. LNEE, vol. 539, pp. 353–360. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04324-7_44
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Rundo, F. et al. (2021). Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_13
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