자동차안전학회지 (Journal of Auto-vehicle Safety Association)

한국자동차안전학회 (Korean Auto-vehicle Safety Association)
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머신러닝/ADAS 정보 활용 충돌안전 제어로직 개발

Development of Collision Safety Control Logic using ADAS information and Machine Learning

  • 투고 : 2022.06.02
  • 심사 : 2022.08.01
  • 발행 : 2022.09.30
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초록

In the automotive industry, the development of automobiles to meet safety requirements is becoming increasingly complex. This is because quality evaluation agencies in each country are continually strengthening new safety standards for vehicles. Among these various requirements, collision safety must be satisfied by controlling airbags, seat belts, etc., and can be defined as post-crash safety. Apart from this safety system, the Advanced Driver Assistance Systems (ADAS) use advanced detection sensors, GPS, communication, and video equipment to detect the hazard and notify driver before the collision. However, research to improve passenger safety in case of an accident by using the sensor of active safety represented by ADAS in the existing passive safety is limited to the level that utilizes the sudden braking level of the FCA (Forward Collision-avoidance Assist) system. Therefore, this study aims to develop logic that can improve passenger protection in case of an accident by using ADAS information and driving information secured before a collision. The proposed logic was constructed based on LSTM deep learning techniques and trained using crash test data.

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참고문헌

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