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Personalized and dynamic rear-view mirror adjustment and profiling with voice signature

Huseyin Karacali1,
Nevzat Donum2,
Efecan Cebel3
1TTTech Auto Turkey
2TTTech Auto Turkey
3TTTech Auto Turkey
Published:December 31, 2023

Abstract

This paper introduces a novel automated system designed for adjusting automobile rear-view mirrors intelligently by utilizing head pose orientation. In today's increasingly personalized car interiors, ensuring the correct alignment of the rear-view mirror based on the driver's head orientation significantly enhances both safety and driving comfort. Manual adjustment of rear-view mirrors can result in issues such as improper angles and driver distractions. This study aims to automate the rear-view mirror adjustment process by accurately detecting the driver's head position and orientation, thereby mitigating these challenges. The system incorporates a camera within the vehicle's cockpit to track the driver. Raw data captured by the camera undergoes processing using the Perspective-n-Point (PnP) algorithm to determine the driver's head position and orientation. The computed positional information is then employed to precisely align the rear-view mirror, optimizing the field of view and eliminating blind spots. Within a brief timeframe, the system establishes the most suitable mirror settings for any driver. Moreover, it dynamically adapts to changes in the driver's posture during driving, ensuring consistently optimal visibility. Additionally, faster and more comfortable use of this in-car smart system is aimed with voice identification technology. Mirror angle values are kept in memory and can be applied instantly at the driver's request, with driver profiles created with voice identifications. Consequently, this research proposes an innovative application that contributes to the integration and personalization of smart technologies, potentially becoming part of forthcoming automotive cockpit designs.

Keywords
Rear-viewsmart cockpitface recognitionvoice identification

References

  1. 1.M. Vamsi, K. P. Soman and K. Guruvayurappan, "Automatic Seat Adjustment using Face Recognition," 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 449-453, doi: 10.1109/ICICT48043.2020.9112538.DOI
  2. 2.Lo, Ei-Wen (Victor) & Green, Paul. (2013). Development and Evaluation of Automotive Speech Interfaces: Useful Information from the Human Factors and the Related Literature. International Journal of Vehicular Technology. 2013. 10.1155/2013/924170.DOI
  3. 3.W. Astuti, S. Tan, M. Solihin, R. Vincent, and B. Michael, “Automatic Voice-Based Recognition For Automotive Headlights Beam Control”, Int. J. Automot. Mech. Eng., vol. 18, no. 1, pp. 8454 –, Mar. 2021.
  4. 4.M. Hasenjäger and H. Wersing, "Personalization in advanced driver assistance systems and autonomous vehicles: A review," 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 2017, pp. 1-7, doi: 10.1109/ITSC.2017.8317803.DOI
  5. 5.Lee, J. M., & Ju, D. Y. (2018). Classification of Human‐Vehicle Interaction: User Perspectives on Design. In Social Behavior and Personality: an international journal (Vol. 46, Issue 7, pp. 1057–1070). Scientific Journal Publishers Ltd. https://doi.org/10.2224/sbp.6242DOI
  6. 6.J. Martindale, “What is a CPU? here’s everything you need to know,” Digital Trends, https://www.digitaltrends.com/computing/what-is-a-cpu.Link
  7. 7.“Central Processing Unit,” Central Processing Unit - an overview | ScienceDirect Topics, https://www.sciencedirect.com/topics/engineering/central-processing-unit.Link
  8. 8.“I.MX 8 Family applications processor: ARM cortex-A53/A72/M4,” i.MX 8 Family Applications Processor | Arm Cortex-A53/A72/M4 | NXP Semiconductors, https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-applications-processors/i-mx-8-family-arm-cortex-a53-cortex-a72-virtualization-vision-3d-graphics-4k-video:i.MX8.Link
  9. 9.A. Klinger, “Embedded linux – kernel, Aufbau, toolchain,” Embedded Software Engineering - Fachwissen, https://www.embedded-software-engineering.de/embedded-linux-kernel-aufbau-toolchain-a-99d15279522f4d1fcd8b2d852a8f771b/.Link
  10. 10.“Software,” Yocto Project, https://www.yoctoproject.org/software-overview/.Link
  11. 11.“About - OpenCV.” https://opencv.org/about/.Link
  12. 12.“WM8960.” [Online]. Available: http://www.sunnyqi.com/upLoadproduct/month_1306/ WM8960.pdf.Link
  13. 13.“A comprehensive guide to Convolutional Neural Networks - the eli5 way,” Saturn Cloud Blog, https://saturncloud.io/blog/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way/.Link
  14. 14.“What are convolutional neural networks?,” IBM, https://www.ibm.com/topics/ convolutional-neural-networks.Link
  15. 15.“What Is a Convolutional Neural Network? - MATLAB & Simulink.” https://www.mathworks.com/discovery/convolutional-neural-network-matlab.html.Link
  16. 16.“Deep Neural Network - an overview | ScienceDirect Topics.” https://www.sciencedirect.com/topics/computer-science/deep-neural-network.Link
  17. 17.“Deep Neural Network: What is it and how is it working?” https://datascientest.com/en/deep-neural-network-what-is-it-and-how-is-it-working.Link
  18. 18.“What is Deep Learning? | IBM.” https://www.ibm.com/topics/deep-learning.Link
  19. 19.“About the Kaldi project - Kaldi.” https://kaldi-asr.org/doc/about.html.Link
  20. 20.“Kaldi Tutorial.” http://eleanorchodroff.com/tutorial/kaldi/.Link
  21. 21.Markus Winkler, Rainer Mehl, Jerome Buvat, Ramya Krishma Puttur, Gaurav Aggarwal, Hiral Shah, Voice on the go. Capgemini Research Institute, 2019
  22. 22.W. Astuti, S. Tan, M. Solihin, R. Vincent, and B. Michael, “Automatic Voice-Based Recognition For Automotive Headlights Beam Control”, Int. J. Automot. Mech. Eng., vol. 18, no. 1, pp. 8454 –, Mar. 2021.
  23. 23.Whittington, Jim, Ye, Hua, Kamalakannan, K, Vu, Ngoc-Vinh, Mason, Michael, Kleinschmidt, Tristan, & Sridharan, Sridha (2010) Low-cost hardware speech enhancement for improved speech recognition in automotive environments. In Doyle, N (Ed.) The 24th ARRB Conference Proceedings. ARRB Group Ltd., CD Rom, pp. 1-17.
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Cite This Article
Karacali, H., Donum, N., Cebel, E. (2023). Personalized and dynamic rear-view mirror adjustment and profiling with voice signature. *The European Journal of Research and Development*, 3(4), 390-413. https://doi.org/10.56038/ejrnd.v3i4.278

Bibliographic Info

JournalThe European Journal of Research and Development
Volume3
Issue4
Pages390–413
PublishedDecember 31, 2023
eISSN2822-2296