Combined regression and classification artificial neural networks for sideslip angle estimation and road condition identification

Bonfitto, Angelo; Feraco, Stefano; Tonoli, Andrea; Amati, Nicola

doi:10.1080/00423114.2019.1645860

Cited by 40 publications

(24 citation statements)

References 30 publications

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“…In this regard, Martino et al employed the principle component analysis (PCA) to reduce the number of dimensions of raw training data [95]. Bonfitto et al [96] presented a NN‐based algorithm in tandem with road condition identification.…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected.

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Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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“…The input of the classifier is a set of 64 predictors, extracted from seven of the acquired signals, namely longitudinal and lateral accelerations (a x and a y ), yaw rate ] o and longitudinal speed of the four wheels (v FL ,v FR ,v RL ,v RR ) [6]. Features from 1 to 22 have a straightforward definition (mean, standard deviation, peak to RMS value and variance for the acquired signals).…”

Section: Classification Task For the Road Condition Identificationmentioning

confidence: 99%

“…speed and sideslip angle) can be directly measured only with expensive, bulky and low robust devices, whose adoption in large production vehicles is not a viable solution. This motivates the considerable research effort that is recently being dedicated to investigation of alternative methods, such as the application of artificial intelligence to the assessment of vehicle dynamics [5][6]. In this paper, the attention is focused on estimation of the vehicle speed, a parameter that plays a key role in several active systems dedicated to control of the wheel slip, yaw rate and sideslip angle [7][8].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method for Vehicle Speed Estimation

Bonfitto¹,

Feraco²

2021

Komunikácie

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This paper presents a method based on Artificial Neural Networks for estimation of the vehicle speed. The technique exploits the combination of two tasks: a) speed estimation by means of regression neural networks dedicated to different road conditions (dry, wet and icy); b) identification of the road condition with a pattern recognition neural network. The training of the networks is conducted with experimental datasets recorded during the driving sessions performed with a vehicle on different tracks. The effectiveness of the proposed approach is validated experimentally on the same car by deploying the algorithm on a dSPACE computing platform. The estimation accuracy is evaluated by comparing the obtained results to the measurement of an optical sensor installed on the vehicle and to the output of another estimation method, based on the mean value of velocity of the four wheels.

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“…Although effective, these methods may suffer severe inaccuracies due to unmodeled dynamics or when the reference model is not tuned to represent all the driving conditions and possible vehicle setups and tuning, which can be a frequent situation when dealing with the high-performance or racing vehicles. To avoid the dependency of the vehicle model, adopting the artificial intelligence, such as Artificial Neural Networks (ANNs) [17][18][19][20][21], represents a possibility. Nevertheless, these solutions are strongly dependent on the network architecture and training datasets, which must include all the possible driving maneuvers and road…”

Section: Issn 1335-4205 (Print Version) Issn 2585-7878 (Online Version)mentioning

confidence: 99%

Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

Bonfitto¹,

Feraco²,

Rossini³

et al. 2021

Komunikácie

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This paper presents a method for the vehicle speed estimation with a Fuzzy Logic based algorithm. The algorithm acquires the measurements of the yaw rate, steering angle, wheel velocities and exploits a set of five Fuzzy Logics dedicated to different driving conditions. The technique estimates the speed exploiting a weighted average of the contributions provided by the longitudinal acceleration and the credibility assigned by the Fuzzy Logics to the measurements of the wheels’ speed. The method is experimentally evaluated on an all-wheel drive electric racing vehicle and is valid for the front and rear wheel drive configurations. The experimental validation is performed by comparing the obtained estimation with the result of computing the speed as the average of the linear velocity of the four wheels. A comparison to the integral of the vehicle acceleration over time is reported.

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Combined regression and classification artificial neural networks for sideslip angle estimation and road condition identification

Cited by 40 publications

References 30 publications

Sideslip angle estimation of ground vehicles: a comparative study

Sideslip angle estimation of ground vehicles: a comparative study

A Data-Driven Method for Vehicle Speed Estimation

Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

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