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학술대회 A Method for Driving Control Authority Transition for Cooperative Autonomous Vehicle
Cited 11 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
저자
구용본, 김진우, 한우용
발행일
201506
출처
Intelligent Vehicles Symposium (IV) 2015, pp.394-399
DOI
https://dx.doi.org/10.1109/IVS.2015.7225717
협약과제
15MC2800, ICT기반 차량/운전자 협력자율주행 시스템(Co-Pilot)의 판단/제어 기술 개발, 한우용
초록
Many researchers have reported that a decline in driving concentration caused by drowsiness or inattentiveness is one of the primary sources of serious car accidents. One of the most well-known methods to measure a driver's concentration is called driver state monitoring, where the driver is warned when he or she is falling asleep based on visual information of the face. On the other hand, autonomous driving systems have garnered attention in recent years as an alternative plan to reduce human-caused accidents. This system shows the possibility of realizing a vehicle with no steering wheel or pedals. However, lack of technical maturity, human acceptance problems, and individual desire to drive highlight the demand to keep human drivers in the loop. For these reasons, it is necessary to decide who will be responsible for driving the vehicle and adjusting the vehicle control system. This is known as the driving control authority. In this paper, we present a system that can suggest transitions in various driving control authority modes by sensing a decline of the human driver's performance caused by drowsiness or inattentiveness. In more detail, we identify the problems of the legacy driving control authority transition made only with vision-based driver state recognition. To address the shortcomings of this method, we propose a new recommendation method that combines the vision-based driver state recognition results and path suggestion of an autonomous system. Experiment results of simulated drowsy and inattentive drivers on an actual autonomous vehicle prototype show that our method has better transition accuracy with fewer false-positive errors compared with the legacy transition method that only uses vision-based driver state recognition.
KSP 제안 키워드
Autonomous driving system, Autonomous system, Autonomous vehicle, Driver state, Driving control, Experiment results, Falling asleep, Human driver, Path suggestion, Primary sources, Recommendation method