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도시 내 다차선에서의 U턴 주행을 위한 보조 지역 경로 생성 알고리즘
백승혁(SeungHyuk Baek),박장현(Jahnghyon Park) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
Recently, due to the development of Autonomous Driving Car, various Advanced Driver Assistance System(ADAS)s are getting a lot of attention. This paper proposes a method for the Advanced Driving Strategy in U-turn driving which is frequent in urban region. As the classical path planning method generates the discrete waypoint which have steering angle constraints, it is not proper for U-turn driving Scenario. So, I suggest Extra Local Path Generation for the matter above. Finally, An Extra optimal parametric path curve will be expected for Autonomous vehicle to drive comfortably in U-turn driving.
확률적 위험도 평가가 고려된 자율 주행 차량에서의 지역 경로 계획 전략
김동환(Donghwan Kim),최영민(Youngmin Choi),박장현(Jahnghyon Park) 한국자동차공학회 2020 한국자동차공학회 학술대회 및 전시회 Vol.2020 No.11
There are numerous latest technologies in autonomous driving. Among them, path planning studies have contributed greatly to avoiding collisions with static objects and finally reaching the target point. However, for this path planning to be applied to vehicles and more intelligent robots, both the effects of dynamic objects (E.g. surrounding vehicles) as well as static, convenience, and safety must be considered. In this study, the risk level of the ego and adjacent lanes is analyzed based on the surrounding vehicle information measured by sensors, and this is optimized and reflected as one of the factors that determine the local path of the vehicle. The main contribution of this study is as follows. First, it is possible to prevent selection even if a path candidate occurs in an area that is not the actual by reflecting the lane information from the risk assessment of the lane to the local path candidate. Second, a path planning strategy can be established considering the situation of lateral rearward regions where local path candidates are not created. As a result, it is expected that vehicles equipped with this path planning strategy will be able to plan safer paths than before.