Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin, Xianjian; Yin, Guodong; Chen, Nan

doi:10.3390/s19194289

Cited by 93 publications

(66 citation statements)

References 163 publications

(269 reference statements)

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“…The tractor for analyzation is simplified as a bicycle model (also called the single-track model (Jin et al 2019)) depicted in Fig. 1.…”

Section: A Linear Vehicle Dynamic Modelmentioning

confidence: 99%

“…We additionally investigate how the proposed tlsDMDc performs on vehicle sates estimation and sensor noise correction. The identified vehicle model integrated with GPS measurements through sensor fusion methods, such as Kalman filter (FK) and extended Kalman filter (EKF) (Chindamo et al 2018;Jin et al 2019), can provide higher states estimate and also correct accumulative errors of INS in the long run.…”

Section: Introductionmentioning

confidence: 99%

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Real-time states estimation of a farm tractor using dynamic mode decomposition

Wang¹,

Noguchi

2020

GPS Solut

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We present a pure data-driven method to estimate vehicle dynamics from the measurements of sideslip and yaw rate in the use of GPS and inertial navigation system. The GPS and INS configuration provides vehicle position, velocity vector, vehicle orientation, and yaw rate observations. A new dynamic mode decomposition with control (DMDc) method denoises the state observations by adopting the total least-squares algorithm. The total least-squares DMD with control (tlsDMDc) helps discover the underlying dynamics with the time-dependent observations of states and external control. The experiments of a simulated linear dynamic model with synthetic Gaussian noise illustrate that the solutions of tlsDMDc are more accurate than the standard DMDc to characterize underlying dynamics with imperfect measurements. We additionally investigate how the algorithm performs on vehicle motion deduction and sensor bias correction. It has been shown that the tlsDMDc-based state estimator with the couple of GPS and inertial sensor measurements provides accurate and robust observation in the presence of model error and measurement noise.

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“…The tractor for analyzation is simplified as a bicycle model (also called the single-track model (Jin et al 2019)) depicted in Fig. 1.…”

Section: A Linear Vehicle Dynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time states estimation of a farm tractor using dynamic mode decomposition

Wang¹,

Noguchi

2020

GPS Solut

View full text Add to dashboard Cite

show abstract

“…On the other hand, in works such as [7], both roll and sideslip angles are estimated by using additional external sensors that need to be integrated into the vehicle instead of using the own on-board systems. In general, these approaches are based on vehicle model-based estimation and data-driven-based estimation [8]. The former group includes approaches such as Kalman-filter-based method [9][10][11], nonlinear-observer-based method [11][12][13], and robust observers [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

Prieto-González

Sánchez

García-Guzmán

et al. 2020

Sensors

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Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

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“…During field operations, the attitude angles of wide-amplitude agricultural machine, especially the roll angle, affect the tillage depth and compressing strength of machine and thereby impact the tillage quality [1,2]. When an agricultural machine is turning or driving on a curved line in farmlands, the accurate roll angle of the implement can provide support for the control of automatic navigation agricultural vehicles [3], and it will prewarn the possible roll before climbing or descending, thereby effectively decreasing the loss of life and properties [4].…”

Section: Introductionmentioning

confidence: 99%

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

et al. 2020

Sensors

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Accurately obtaining roll angles is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. Given the demand for the acquisition of agricultural equipment roll angles, a roll angle monitoring model based on Kalman filtering and multi-source information fusion was established by using the MTi-300 AHRS inertial sensor (INS) and XW-GI 5630 BeiDou Navigation Satellite System (BDS), which were installed on agricultural equipment. Data of the INS and BDS were fused by MATLAB; then, Kalman filter was used to optimize the data, and the state equation and measurement equation of the integrated system were established. Then, an integrated monitoring terminal man-machine interactive interface was designed on MATLAB GUI, and a roll angle monitoring system based on the INS and BDS was designed and applied into field experiments. The mean absolute error of the integrated monitoring system based on multi-source information fusion during field experiments was 0.72 • , which was smaller compared with the mean absolute errors of roll angle monitored by the INS and BDS independently (0.78 • and 0.75 • , respectively). Thus, the roll angle integrated model improves monitoring precision and underlies future research on navigation and independent operation of agricultural equipment.

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Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Cited by 93 publications

References 163 publications

Real-time states estimation of a farm tractor using dynamic mode decomposition

Real-time states estimation of a farm tractor using dynamic mode decomposition

Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

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