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Tactile Occupant Detection Sensor for Automotive Airbag

Author

Listed:
  • Naveen Shirur

    (CARISSMA, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany
    Institut für Fahrzeugtechnik, Technische Universität Braunschweig, Hans-Sommer-Straße 4, 38106 Braunschweig, Germany)

  • Christian Birkner

    (CARISSMA, Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany)

  • Roman Henze

    (Institut für Fahrzeugtechnik, Technische Universität Braunschweig, Hans-Sommer-Straße 4, 38106 Braunschweig, Germany)

  • Thomas M. Deserno

    (Peter L. Reichertz Institut für Medizinische Informatik, Technische Universität Braunschweig, Mühlenpfordtstraße 23, 38106 Braunschweig, Germany)

Abstract

Automotive airbags protect occupants from crash forces during severe vehicle collisions. They absorb energy and restrain the occupants by providing a soft cushion effect known as the restraint effect. Modern airbags offer partial restraint effect control by controlling the bag’s vent holes and providing multi-stage deployment. Full restraint effect control is still a challenge because the closed-loop restraint control system needs airbag–occupant contact and interaction feedback. In this work, we have developed novel single and matrix capacitive tactile sensors to measure the occupant’s contact data. They can be integrated with the airbag surface and folded to follow the dynamic airbag shape during the deployment. The sensors are tested under a low-velocity pendulum impact and benchmarked with high-speed test videos. The results reveal that the single sensor can successfully measure occupant–airbag contact time and estimate the area, while the contact position is additionally identified from the matrix sensor.

Suggested Citation

  • Naveen Shirur & Christian Birkner & Roman Henze & Thomas M. Deserno, 2021. "Tactile Occupant Detection Sensor for Automotive Airbag," Energies, MDPI, vol. 14(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5288-:d:622202
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    Citations

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    Cited by:

    1. Marek Guzek & Rafał S. Jurecki & Wojciech Wach, 2022. "Vehicle and Traffic Safety," Energies, MDPI, vol. 15(13), pages 1-4, June.
    2. Muhammad S. Aliero & Muhammad F. Pasha & David T. Smith & Imran Ghani & Muhammad Asif & Seung Ryul Jeong & Moveh Samuel, 2022. "Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques," Energies, MDPI, vol. 15(23), pages 1-22, December.

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