Vehicular trajectory estimation utilizing slip angle based on GNSS Doppler/IMU

Abstract

Accurate vehicular trajectory estimation is important for the recently developed autonomous driving systems. As the accuracy of the vehicular trajectory estimation is reduced with the slippage that occurs during turning, we propose a method in this study to accurately estimate the trajectory of a vehicle, focusing on the slip angle estimation. Although the two-wheel model is used as a general concept slip angle estimation, the accurate estimation of the parameters was difficult using the conventional methods. Therefore, a global navigation satellite system (GNSS) Doppler was used for parameter estimation. In addition, the roll angle was estimated as it occurs during turning and affects the slip angle of the vehicle. Specifically, we verified the improvement in accuracy of the vehicular trajectory estimation using the cost-effective GNSS Doppler/IMU.

Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter

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Vehicle state and parameter estimation based on adaptive robust unscented particle filter

“…Malikov [11] solved the problem of differential equations with periodic using the quadratic Lyapunov function. Takikawa1 et al [12] used a global navigation satellite system (GNSS) Doppler for accurate vehicular trajectory estimation. Gao et al [13,14] presented a new methodology of distributed state fusion for multisensory nonlinear systems by using the sparse-grid quadrature filter.…”