Objective: The purpose of this research is to conduct an experiment on the human factor of control authority transition and analyze the performance of control authority transition by using a vehicle simulator to support the driver's take-over mechanism in level 3 automated vehicles.Background: How to inform the transition of control to manual driving, the driver's NDRTs (Non-Driving Related Tasks), the driver's age, and driving experience can affect the quality and timing of manual driving re-engagement. Therefore, research on human factors for control authority transition is needed in level 3 automated vehicles.Method: We conducted experiments to identify how visual and cognitive workloads, pre-cue for attention shifts, driving situation information, modality types that provide TOR (Take Over Request) information, driving readiness, and driver's secondary task types affect control authority transition.Results: In this study, we found that pre-cue or driving situation information is provided before TOR, the performance for driver's take-over is improved. When pre-cue is provided auditory (4.25s), the time to recognize TOR is significantly faster than when it is provided visually (6.25s). We found that the case of providing driving situation awareness information (3.19s) is faster than the case of not providing (3.96s).We also identified that the performance for driver's take-over is improved when haptic interactions are added to provide TOR information. Adding a haptic modality (3.75s) to an auditory interaction to provide a TOR notification has been observed to have a much faster TOR recognition time than adding a visual modality (4.57s). In addition, we found that the greater the cognitive, visual, auditory, and hand-based physical demands related to the type of secondary tasks (NDRTs), the greater the workload felt by the driver, so it takes a long time to recognize the TOR and the performance for control authority transitions is lowered. The TOR recognition time was found to be faster in cases of looking ahead or around (2.74s) than in the case of drinking task (3.12s) or texting (3.23s).Conclusion: The level 3 automated vehicles must manage the driver's readiness to drive at any time so that the driver can regain control from the ADS and engage in driving. To this end, it is necessary to continuously develop a driver monitoring system and related technologies that measure the driver's gaze, hand movement, in-vehicle conversation, and seating information in real-time.Application: The results of this research can be used for the development of guidelines and commercialization policies that can be referenced and applied by Level 3 automated vehicle companies and organizations related to automated driving.
KSP 제안 키워드
Automated driving, Automated vehicles, Driver Monitoring System, Driving experience, Haptic Interaction, Human Factors, In-vehicle, Level 3, Long Time, Non-driving related tasks(NDRT), Physical demands
저작권정책 안내문
한국전자동신연구원 지식공유플랫폼 저작권정책
한국전자통신연구원 지식공유플랫폼에서 제공하는 모든 저작물(각종 연구과제, 성과물 등)은 저작권법에 의하여 보호받는 저작물로 무단복제 및 배포를 원칙적으로 금하고 있습니다. 저작물을 이용 또는 변경하고자 할 때는 다음 사항을 참고하시기 바랍니다.
저작권법 제24조의2에 따라 한국전자통신연구원에서 저작재산권의 전부를 보유한 저작물의 경우에는 별도의 이용허락 없이 자유이용이 가능합니다. 단, 자유이용이 가능한 자료는 "공공저작물 자유이용허락 표시 기준(공공누리, KOGL) 제4유형"을 부착하여 개방하고 있으므로 공공누리 표시가 부착된 저작물인지를 확인한 이후에 자유이용하시기 바랍니다. 자유이용의 경우에는 반드시 저작물의 출처를 구체적으로 표시하여야 하고 비영리 목적으로만 이용이 가능하며 저작물을 변형하거나 2차 저작물로 사용할 수 없습니다.
<출처표시방법 안내> 작성자, 저작물명, 출처, 권호, 출판년도, 이용조건 [예시1] 김진미 외, "매니코어 기반 고성능 컴퓨팅을 지원하는 경량커널 동향", 전자통신동향분석, 32권 4호, 2017, 공공누리 제4유형 [예시2] 심진보 외, "제4차 산업 혁명과 ICT - 제4차 산업 혁명 선도를 위한 IDX 추진 전략", ETRI Insight, 2017, 공공누리 제 4유형
공공누리가 부착되지 않은 자료들을 사용하고자 할 경우에는 담당자와 사전협의한 이후에 이용하여 주시기 바랍니다.