Cascade Extended Kalman Filter 기반의 차량동특성 및 도로종단경사 추정
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Vehicle dynamic states used in various advanced driving safety systems are influenced by road geometry. Among the road geometry parameters, the vehicle pitch angle influenced by road slope and acceleration-deceleration is essential parameters used in pose estimation, advanced adaptive cruise control and others on sag road. Although the road slope data is essential data, the method measuring the data is not commercialized. The digital map including the road geometry data and high-precision DGPS system such as DGPS(Differential Global Positioning System) based RTK(Real-Time Kinematics) are used unusually. In this paper, low-cost cascade extended Kalman filter(CEKF) based vehicle pitch angle estimation method is proposed. It use two cascade EKFs. The EKFs use several measured vehicle states, for example yaw rate, longitudinal acceleration, lateral acceleration and wheel speed of the rear tiers and 3 D.O.F(Degree Of Freedom) vehicle dynamics model. The performance of proposed estimation algorithm is evaluated by simulation based on Carsim dynamics tool and T-car based experiment.
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