Vehicle position estimation using nonlinear tire model for autonomous vehicle

Yoon, Jaewoo; Kim, Byeong-Woo

doi:10.1007/s12206-016-0705-5

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Next, the longitudinal slip ratio and the sideslip angle are calculated by (14) and (15). Note that, here we only give the calculation equations for the front wheel.…”

Section: Vdmmentioning

confidence: 99%

“…Four methods are used in the experiment, namely: INS, VKM aided INS [21], VDM aided INS [15,22], and the multilayer VMAINS proposed in this paper. They are labeled as method#1-#4.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…Reference [14] analyzed navigation accuracies of GNSS/INS, GNSS/INS/VDM, and GNSS/VDM, and concluded that: if low-end inertial sensors are adopted, navigation accuracy can be improved no matter whether VDM is used as an aid or INS instead. Reference [15] used VDM with the nonlinear tire model and combined VDM with GNSS to serve as the basis of ADAS. Reference [16] used three observers based on INS and VDM in the navigation system, then switched the observers according to the driving state, to calculate the vehicle attitude and velocity, so that the influence of the single model failure can be eliminated.…”

Section: Introductionmentioning

confidence: 99%

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Multi-layer model aided inertial navigation system for unmanned ground vehicles

Shi

Wang

et al. 2022

Meas. Sci. Technol.

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The inertial navigation system (INS) has been commonly adopted by unmanned ground vehicles (UGV), but it needs other systems to correct the error divergence, such as the Global Navigation Satellite System (GNSS). However, GNSS failures are inevitable, and INS must navigate independently in such situations, that the navigation errors will diverge fast. To solve this problem, the vehicle model aided INS is proposed. In this paper, three commonly used vehicle models are analyzed to conclude their disadvantages first, that contains vehicle kinematics model (VKM), the non-holonomic constraint (NHC) of VKM, and the vehicle dynamics model (VDM). Against their disadvantages, the multi-layer vehicle model aided INS is proposed. The proposed method divides INS error parameters into the sensor layer, system layer I, and system layer II. Then, the navigation information from INS is fused with the developed VKM and VDM at the system layer and the sensor layer respectively. Besides, the adaptive Kalman filter is designed based on the VKM error model, that the estimations can be protected from the observation errors when the VKM accuracy declines. Compared to the traditional vehicle model aided INS, the proposed method can improve the accuracy and robustness of the navigation system with acceptable computational complexity.

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“…Next, the longitudinal slip ratio and the sideslip angle are calculated by (14) and (15). Note that, here we only give the calculation equations for the front wheel.…”

Section: Vdmmentioning

confidence: 99%

“…Four methods are used in the experiment, namely: INS, VKM aided INS [21], VDM aided INS [15,22], and the multilayer VMAINS proposed in this paper. They are labeled as method#1-#4.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-layer model aided inertial navigation system for unmanned ground vehicles

Shi

Wang

et al. 2022

Meas. Sci. Technol.

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“…The actual driving radar sensor data was used by Moon et al to verify the optimization of the state estimation performance of the vehicle body in the process of target tracking [12]. Yoon et al employed GPS and inertial measurement unit (IMU) to fuse the signals of sensors, and proposes a high-precision correction method of position error [13].…”

Section: Introductionmentioning

confidence: 99%

State observation of nonlinear off-road vehicle system under complex maneuver condition

et al. 2020

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The information of vehicle attitude and tire force under complex environment and maneuver condition is of great significance for system risk prediction and active control system intervention. In order to collect the accurate system states, the coupling vehicle dynamics model and moving horizon estimation method are employed to solve the online optimization problem based on the premise of rolling optimization. Furthermore, the accurate observation and acquisition of the vehicle system state are realized. On this basis, the simulation process of the vehicle state observation using moving horizon estimation method and unscented Kalman filter algorithm are implemented, respectively. The corresponding observation results under complex maneuvering conditions are further validated by using the hardware-in-the-loop experimental platform. Finally, the comparison of the observation results obtained by the unscented Kalman filter and moving horizon estimation algorithms demonstrate that the moving horizon estimation method can effectively improve the observation accuracy of vehicle system state in complex environment, including vehicle roll angle and tire dynamic force. The results obtained through moving horizon estimation method are conducive to the further signal early warning, risk prediction and assessment, as well as systematic intervention and active rollover control.

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“…The topic of INS- and GNSS-based sensor fusion for different applications in ground or air vehicles was a large scope in the research of the last decades. A quiet comprehensive overview of methods such as dead reckoning can be found in the literature, for example, [8,9,10]. Moreover, in the literature [11], time delayed sensor fusion for sigma point Kalman filters (SPKF) is presented.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation

Brembeck

2019

Sensors

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The design of high–performance state estimators for future autonomous vehicles constitutes a challenging task, because of the rising complexity and demand for operational safety. In this application, a vehicle state observer with a focus on the estimation of the quantities position, yaw angle, velocity, and yaw rate, which are necessary for a path following control for an autonomous vehicle, is discussed. The synthesis of the vehicle’s observer model is a trade-off between modelling complexity and performance. To cope with the vehicle still stand situations, the framework provides an automatic event handling functionality. Moreover, by means of an efficient root search algorithm, map-based information on the current road boundaries can be determined. An extended moving horizon state estimation algorithm enables the incorporation of delayed low bandwidth Global Navigation Satellite System (GNSS) measurements—including out of sequence measurements—as well as the possibility to limit the vehicle position change through the knowledge of the road boundaries. Finally, different moving horizon observer configurations are assessed in a comprehensive case study, which are compared to a conventional extended Kalman filter. These rely on real-world experiment data from vehicle testdrive experiments, which show very promising results for the proposed approach.

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Vehicle position estimation using nonlinear tire model for autonomous vehicle

Cited by 11 publications

References 17 publications

Multi-layer model aided inertial navigation system for unmanned ground vehicles

Multi-layer model aided inertial navigation system for unmanned ground vehicles

State observation of nonlinear off-road vehicle system under complex maneuver condition

Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation

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