Road profile and suspension state estimation boosted with vehicle dynamics conjectures

Vazquez, Angel Gabriel Alatorre; Vaseur, Cyrano; Victorino, Alessandro Corrêa; Charara, Ali

doi:10.1109/ivs.2019.8814242

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Out of these forces, relative suspension velocity is derived. Vazquez et al [24] provided suspension state, road profile and transfer load estimation methodology using deflection sensors, accelerometers and gyrometers. The observation scheme used is a linear Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Šabanovič¹,

Kojis²,

Šukevičius³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the automotive industry moving towards automated driving, sensing is becoming an increasingly important part of enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors for the estimation of vehicle unsprung mass relative velocity.

show abstract

Section: Introductionmentioning

confidence: 99%

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Šabanovič¹,

Kojis²,

Šukevičius³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Out of these forces, relative suspension velocity is derived. Vazquez et al [28] provided a suspension state, road profile and transfer load estimation methodology using deflection sensors, accelerometers and gyrometers. The observation scheme used is a linear Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Šabanovič

Kojis

Šukevičius

et al. 2021

Sensors

View full text Add to dashboard Cite

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

show abstract

A study on a vehicle semi-active suspension control system based on road elevation identification

Yang

Shi²,

Zheng³

et al. 2022

PLoS ONE

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A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is key to achieving semi-active suspension. To improve the road elevation perception ability of vehicles, this study proposes a continuous multiple scanning recursive matching algorithm based on a single-line LIDAR sensor. Radar recursive scanning is used to obtain the multiple superposition data of echo signals, and coordinate matching is realized between historical scanning data and current scanning data. Simultaneously, the sensor height deviation and pitch angle deviation of the sensors are regressed to obtain an accurate pavement elevation. Considering the control effect of the active vehicle suspension, a vehicle suspension model with seven degrees of freedom is established. The semi-active suspension controller is constructed using a diagonal recursive neural network algorithm, and the neural network weight is trained using a genetic algorithm. In addition, a preview diagonal recursive neural network control strategy for semi-active suspension, based on the combination of road elevation information, is proposed. The results of a hardware-in-the-loop co-simulation, which was conducted based on the Simulink control model and dSPACE real-time simulation, revealed that the ride comfort and stability of the vehicle were improved owing to a preview of the elevation information of the road ahead and the active adjustment of the shock absorber of the suspension system.

show abstract

Road profile and suspension state estimation boosted with vehicle dynamics conjectures

Cited by 3 publications

References 15 publications

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Feasibility of Neural Network Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

Feasibility of a Neural Network-Based Virtual Sensor for Vehicle Unsprung Mass Relative Velocity Estimation

A study on a vehicle semi-active suspension control system based on road elevation identification

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