• Title/Summary/Keyword: 긴급제동 보조 시스템

Search Result 3, Processing Time 0.015 seconds

Development of Personal Mobility Safety Driving Assistance System Using CNN-Based Object Detection and Boarding Detection Sensor (합성곱 신경망 기반 물체 인식과 탑승 감지 센서를 이용한 개인형 이동수단 주행 안전 보조 시스템 개발)

  • Son, Kwon Joong;Bae, Sung Hoon;Lee, Hyun June
    • Journal of the Korea Convergence Society
    • /
    • v.12 no.10
    • /
    • pp.211-218
    • /
    • 2021
  • A recent spread of personal mobility devices such as electric kickboards has brought about a rapid increase in accident cases. Such vehicles are susceptible to falling accidents due to their low dynamic stability and lack of outer protection chassis. This paper presents the development of an automatic emergency braking system and a safe starting system as driving assistance devices for electric kickboards. The braking system employed artificial intelligence to detect nearby threaening objects. The starting system was developed to disable powder to the motor until when the driver's boarding is confirmed. This study is meaningful in that it proposes the convergence technology of advanced driver assistance systems specialized for personal mobility devices.

A Study on the Simulation Modeling Method of LKAS Test Evalution (LKAS 시험평가의 시뮬레이션 모델링 기법에 관한 연구)

  • Bae, Geon-Hwan;Lee, Seon-bong
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.21 no.3
    • /
    • pp.57-64
    • /
    • 2020
  • The leading technologies of the ADAS (Advanced Driver Assist System) are ACC (Advanced Cruise Control), LKAS (Lane Keeping Assist System), and AEB (Autonomous Emergency Braking). LKAS is a system that uses cameras and infrared sensors to control steering and return to its running lane in the event of unintentional deviations. The actual test is performed for a safety evaluation and verification of the system. On the other hand, research on the system evaluation method is insufficient when an additional steering angle is applied. In this study, a model using Prescan was developed and simulated for the scenarios proposed in the preceding study. Comparative analyses of the simulation and the actual test were performed. As a result, the modeling validity was verified. A difference between the front wheels and the lane occurred due to the return velocity. The results revealed a maximum error of 0.56 m. The error occurred because the lateral velocity of the car was relatively small. On the other hand, the distance from wheels to the lanes displayed a tendency of approximately 0.5 m. This can be verified reliably.

Study on Effectiveness of Accident Reduction Depending on Autonomous Emergency Braking System (AEB 장치에 대한 사고경감 효과 연구)

  • Choi, JunYoung;Kang, SeungSu;Park, EunAh;Lee, KangWon;Lee, SiHun;Cho, SooKang;Kwon, YoungGil
    • Journal of Auto-vehicle Safety Association
    • /
    • v.11 no.2
    • /
    • pp.6-10
    • /
    • 2019
  • This paper describes effectiveness of accident reduction on vehicles equipped with AEB using accident data occurring in Korea. During the statistical period, we used the number of vehicles which are covered by auto insurance and the number of accidents. To maximize the reduction effect of accidents caused by the driver's carelessness, the analysis was limited to Physical Damage Coverage that covers the cost of repairing or replacing the damaged vehicle caused by the driver's fault. Due to Personal Information Protection Law, it was not capable of comparing the same vehicle using Vehicle Identification Number in this study. Instead of that, we used it as a similar vehicle, so there are limits to the comparison and analysis results. As a result of this study, we have found that the effect of reducing accidents was different depending on the vehicle class, but it was generally concluded that the number of accidents decreased when the vehicle was equipped with an AEB system. Domestic research on the AEB effect of reducing accidents is not active yet. Therefore, it is absolutely essential to analyze the effects according to various conditions such as driver's age, occupation and gender as well as expanding the study models in the future.