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학술지 Decision-Making Framework for Automated Driving in Highway Environments
Cited 16 time in scopus
저자
노삼열, 안경환
발행일
201801
출처
IEEE Transactions on Intelligent Transportation Systems, v.19 no.1, pp.58-71
ISSN
1524-9050
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TITS.2017.2691346
협약과제
17HS1800, 스마트카의 자율주행을 위한 실시간 센싱융합처리가 가능한 커넥티드 드라이빙 컴퓨팅 시스템 기술 개발, 김성훈
초록
© 2011 IEEE. This paper presents a decision-making framework for automated driving in highway environments. The framework is capable of reliably, robustly assessing a given highway situation (with respect to the possibility of collision) and of automatically determining an appropriate maneuver for the situation. It consists of two main components: Situation assessment and strategy decision. The situation assessment component utilizes multiple complementary 'threat measures' and Bayesian networks in its calculations of 'threat levels' at the car and lane level to evaluate the possibility of collisions for a given highway traffic situation. The strategy decision component, designed to generate goal-directed and collision-free behaviors, automatically determines an appropriate maneuver in a given highway situation via a hierarchical state machine-such a machine both reduces the complexity of and extends a strategy model. The types of maneuver determined by the component include both simple maneuvers, such as slowing down to avoid collision with a vehicle in front, and complex maneuvers, such as lane changes and overtaking. The presented decision-making framework is tested and evaluated-both on a closed high-speed test track in simulated traffic with various driving scenarios and on public highways in real traffic through in-vehicle testing-to verify that it can provide sufficiently reliable performance for automated driving in highway environments in terms of safety, reliability, and robustness.
키워드
Automated vehicle, decision-making, probabilistic reasoning, situation assessment, strategy decision
KSP 제안 키워드
Assessment component, Automated driving, Automated vehicles, Avoid collision, Bayesian Network(BN), Hierarchical state machine, High Speed, Highway traffic, In-vehicle, Situation Assessment, Test track