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Real-time estimation of the vehicle sideslip angle through regression based on principal component analysis and neural networks

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... Recently, neural network techniques have been vigorously applied to estimating various vehicle states [14]- [21]. Neural network structures can overcome the limitations of empirical methods because they can identify data characteristics that a human observer cannot detect [22]. ...
... Another effort attempted to solve the lateral state estimation problem of a preceding target vehicle using multiple neural networks, consisting of a nonlinear autoregressive exogenous model net, feedforward net, and Elman net [18]. In addition, hybrid approaches, combining a neural network with other methods, have been applied to estimate vehicle states [19]- [21]. Vehicle roll dynamics based unscented Kalman filter coupled with an artificial neural network provided a good estimation of vehicle roll angles [19]. ...
... A recurrent neural network combined with a vehicle kinematic model was trained using simulation data to estimate vehicle sideslip angles [20]. A principal component analysis was adopted for the preprocessing of input data of a neural network to estimate vehicle sideslip angles [21]. These methods reportedly lowered computational loads with increased accuracy. ...
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This study employs a dual deep neural network (D-DNN) to accurately estimate the absolute longitudinal speed of a vehicle. Accuracy in speed estimation is crucial for vehicle safety, because longitudinal speed is a common parameter employed as a state variable in active safety systems such as anti-lock braking system and traction control system. In this study, DNNs are applied to determine the gain of an adaptive filter to estimate vehicle speed. The used data consists of longitudinal acceleration, wheel speed, filter gain, and estimated vehicle speed. The data generated from Carsim software are collected and preprocessed using a Simulink model. To acquire data with numerous wheel slip patterns, various acceleration and deceleration conditions are applied to four different road conditions. Though, it is challenging to achieve a single DNN model that is optimally cope with the various driving situations. Thus, we adopt two DNN models that were individually trained in low and high acceleration regions. The dual DNN model results in error reductions of 74% and 65%, compared with a single DNN and classical adaptive Kalman filter approaches, respectively.
... To improve estimation robustness, data preprocessing or vehicle state identification is considered. 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. ...
... In order to reduce the number of model input features and improve computational efficiency, a selection of signals with high correlation is necessary. The PCA method is used to verify whether the parameters have high relevance with sideslip angle [95]. In this work, since the PCA is not suitable for online estimation in the VCU, the covariance matrix is used to analyse the correlation between different parameters and vehicle sideslip angle, in which high eigenvalue indicates high correlation and vice versa. ...
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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 paper 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 maneuvers. The results show that 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 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 maneuvers once the training datasets are properly selected.
... The first sensor applied to the experimental setup is the Kistler S-Motion. It combines a complete inertial platform (with 3-axis accelerometers and gyroscopes) with a lamp module that can obtain a direct measure of the longitudinal and lateral velocities by processing a sequence of road images using the correvit principle [36,37]. To do so, the lamp module must be positioned at a 350±50 mm height facing the road, condition achieved in this case by a suction fixture in the rear door of the vehicles (Figure 13). ...
... The developed algorithm, based on the T.R.I.C.K. methodology described in [23], allows us to evaluate in a specifically dedicated on-board module the fundamental kinematic and dynamic quantities for the tire characterization in real time, starting from the experimental signals available within the vehicle CAN bus (Controller Area Network) and s-motion measurement or, as the case in exam, employing a specific set of sensors pre-configured on the vehicle. Such methodology also allows us to evaluate the potential of an estimation process in terms of tire interaction curves, such as in [48]. ...
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In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).
... The performance of the monitoring system was verified with field tests. De Martino et al. [8] presented a method for real-time estimation of the vehicle sideslip angle based on principal component analysis and a neural network, the inputs of which were the steering wheel angle, lateral and longitudinal acceleration, wheel angular velocity, and yaw rate. The effectiveness of the method was verified by a comparison of the sideslip angle provided by an onboard optical Correvit sensor (Kistler Instrument Corp., USA). ...
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... signals that are implicitly correlated with vehicle sideslip angle are available from the Carsim. In order to reduce the number of model inputs and improve computational efficiency, the PCA method is used to select the most highly relevant signals[15]. The covariance matrix is used to analyze the correlation between different signals with vehicle sideslip angle, in which high eigenvalue indicates high correlation and vice versa. ...
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A Numerical–physical tyre model was developed . The whole model allows to obtain the road–tyre interactions so it can be used in vehicle dynamic simulations. In this article are presented its capabilities in normal interaction analysis. The normal interaction, i.e. the relationship between the normal load and the normal deflection, influences the tangential (longitudinal plus lateral) one, which determines the vehicle handling behaviour. The parameters used in this model depend on the structure of the tyre and they can be measured on the real tyre. The tyre has been schematized as composed by a flexible belt , the sidewalls and a rigid ring (Rim). The flexible belt is composed by a number of lumped masses linked by extensional and bending stiffnesses and dampers. The tyre model has been developed using the finite segment method. Using these method could be possible to include in the tyre simulations various non-linear structural effects due to large displacements and rotations. The model allows to simulate both steady state and transient conditions.
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This paper deals with in curve vehicle lateral behaviour and is aimed to find out which vehicle physical characteristics affect significantly its stability. Two different analytical methods, one numerical (phase plane) and the other graphical (handling diagram) are discussed. The numerical model refers to the complete quadricycle, while the graphical one refers to a bicycle model. Both models take into account lateral load transfers and nonlinear Pacejka tyre road interactions. The influence of centre of mass longitudinal position, tyre cornering stiffness and front/rear roll stiffness ratio on vehicle stability are analyzed. The presented results are in good agreement with theoretical expectations about the above parameters influence, and show how some physical characteristics behave as saddle node bifurcation parameters.
Article
Adaptive Cruise Control systems have been developed and introduced into the consumer market for over a decade. Among these systems, fully-adaptive ones are required to adapt their behaviour not only to traffic conditions but also to drivers’ preferences and attitudes, as well as to the way such preferences change for the same driver in different driving sessions. This would ideally lead towards a system where an on-board electronic control unit can be asked by the driver to calibrate its own parameters while he/she manually drives for a few minutes (learning mode). After calibration, the control unit switches to the running mode where the learned driving style is applied. The learning mode can be activated by any driver of the car, for any driving session and each time he/she wishes to change the current driving behaviour of the cruise control system.The modelling framework which we propose to implement comprises four layers (sampler, profiler, tutor, performer). The sampler is responsible for human likeness and can be calibrated while in learning mode. Then, while in running mode, it works together with the other modelling layers to implement the logic. This paper presents the overall framework, with particular emphasis on the sampler and the profiler that are explained in full mathematical detail. Specification and calibration of the proposed framework are supported by the observed data, collected by means of an instrumented vehicle. The data are also used to assess the proposed framework, confirming human-like and fully-adaptive characteristics.
Article
The early evolution of inerual navigation, then seen as an almost-impossible, barely-affordable technology, was driven by the requirements of strategic missile guidance. Since then, several advances in instruments and system techniques have brought it to everyday use in aircraft, ships and land vehicles, whilst still remaining a fascinating and challenging field of engineering. The paper reviews the principles of inertial navigation, and charts its progress from the early gimballed systems, to today's strapdown systems using ring laser gyros. Progress is illustrated by Marconi's inertial products, and forecasts of future direction are presented.
Book
Vehicle Dynamics and Control provides a comprehensive coverage of vehicle control systems and the dynamic models used in the development of these control systems. The control system topics covered in the book include cruise control, adaptive cruise control, ABS, automated lane keeping, automated highway systems, yaw stability control, engine control, passive, active and semi-active suspensions, tire models and tire-road friction estimation. In developing the dynamic model for each application, an effort is made to both keep the model simple enough for control system design but at the same time rich enough to capture the essential features of the dynamics.
Article
Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis (CA) in order to handle qualitative variables and as multiple factor analysis (MFA) in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition (SVD) of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.For further resources related to this article, please visit the WIREs website.
Chapter
Neural networks are making their ways into various commercial products across many industries. As in aerospace, in automotive industry they are not the main technology. Automotive engineers and researchers are certainly familiar with the buzzword, and some have even tried neural networks for their specific applications as models, virtual sensors, or controllers (see, e.g., [1] for a collection of relevant papers). In fact, a quick search reveals scores of recent papers on automotive applications of NN, fuzzy, evolutionary and other technologies of computational intelligence (CI); see, e.g., [2], [3], [4]. However, such technologies are mostly at the stage of research and not in the mainstream of product development yet. One of the reasons is “black-box” nature of neural networks. Other, perhaps more compelling reasons are business conservatism and existing/legacy applications (trying something new costs money and might be too risky) [5], [6].
Conference Paper
The vehicle sideslip angle is one of the most important variable to evaluate vehicle stability during dynamic manoeuvres. In this paper a nonlinear estimator is proposed, which use measurements of lateral accleration, steering angle, yaw rate and longitudinal velocity as input signals and provide the sideslip angle estimate as output. The design of such an estimator is based on a recently proposed direct approach to the design of virtual sensor. The obtained estimator has been experimentally tested on a huge number of different manoeuvres showing quite good results in a large range of operation covering both the linear and the nonlinear behaviour of the car.
Conference Paper
This paper proposes an effective model-based approach to estimate vehicle linear sideslip online via recursive least square method (RLS) with forgetting. In this approach, a Luenberger observer is first designed to estimate vehicle states, including vehicle sideslip. Two lumped vehicle parameters in this observer are updated recursively to minimize the discrepancy between the model used and the physical plant and any possible effects caused by external unknown disturbances, in particular, road surface. Computer simulation and in-vehicle testing have been conducted to verify the proposed approach with results indicating that the proposed approach is very effective and robust in estimating vehicle linear sideslip under various road surfaces
Article
Real-time knowledge of the slip angle in a vehicle is useful in many active vehicle safety applications, including yaw stability control, rollover prevention, and lane departure avoidance. Sensors to measure slip angle, including two-antenna GPS systems and optical sensors, are too expensive for ordinary automotive applications. This paper develops a real-time algorithm for estimation of slip angle using inexpensive sensors normally available for yaw stability control applications. The algorithm utilizes a combination of model-based estimation and kinematics-based estimation. Compared with previously published results on slip angle estimation, this present paper compensates for the presence of road bank angle and variations in tire-road characteristics. The developed algorithm is evaluated through experimental tests on a Volvo XC90 sport utility vehicle. Detailed experimental results show that the developed system can reliably estimate slip angle for a variety of test maneuvers.
Conference Paper
The application of a Moving Horizon Estimator (MHE) to the problem of vehicle side-slip angle estimation is studied. In particular, this work focuses on how MHEs can be designed in order to provide estimates of the variable of interest with guaranteed estimation error. The problem is solved using a nonlinear regression observer identified directly from data.
Article
Automotive engines are multivariable system with severe non-linear dynamics, and their modelling and control are challenging tasks for control engineers. Current control of engine used look-up table combined with proportional and integral (PI) control and is not robust to system uncertainty and time varying effects. In this paper the model predictive control strategy is applied to engine air/fuel ratio control using neural network model. The neural network model uses information from multivariables and considers engine dynamics to do multi-step ahead prediction. The model is adapted in on-line mode to cope with system uncertainty and time varying effects. Thus, the control performance is more accurate and robust compared with non-adaptive model based methods. To speed up algorithm calculation, different optimisation algorithms are investigated and performance compared. Finally, the developed method is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results demonstrate the effectiveness of the developed method.
Article
A model of a neural system where a group of neurons projects to another group of neurons is discussed. We assume that a trace is the simultaneous pattern of individual activities shown by a group of neurons. We assume synaptic interactions add linearly and that synaptic weights (quantitative measure of degree of coupling between two cells) can be coded in a simple but optimal way where changes in synaptic weight are proportional to the product of pre-and postsynaptic activity at a given time. Then it is shown that this simple system is capable of “memory” in the sense that it can (1) recognize a previously presented trace and (2) if two traces have been associated in the past (that is, if trace f̄ was impressed on the first group of neurons and trace ḡ was impressed on the second group of neurons and synaptic weights coupling the two groups changed according to the above rule) presentation of f̄ to the first group of neurons gives rise to f̄ plus a calculable amount of noise at the second set of neurons. This kind of memory is called an “interactive memory” since distinct stored traces interact in storage. It is shown that this model can effectively perform many functions. Quantitative expressions are derived for the average signal to noise ratio for recognition and one type of association. The selectivity of the system is discussed. References to physiological data are made where appropriate. A sketch of a model of mammalian cerebral cortex which generates an interactive memory is presented and briefly discussed. We identify a trace with the activity of groups of cortical pyramidal cells. Then it is argued that certain plausible assumptions about the properties of the synapses coupling groups of pyramidal cells lead to the generation of an interactive memory.
Article
Machines degrade as a result of aging and wear, which decreases performance reliability and increases the potential for faults and failures. The impact of machine faults and failures on factory productivity is an important concern for manufacturing industries. Economic impacts relating to machine availability and reliability, as well as corrective (reactive) maintenance costs, have prompted facilities and factories to improve their maintenance techniques and operations to monitor machine degradation and detect faults. This paper presents an innovative methodology that can change maintenance practice from that of reacting to breakdowns, to one of preventing breakdowns, thereby reducing maintenance costs and improving productivity. To analyze the machine behavior quantitatively, a pattern discrimination model (PDM) based on a cerebellar model articulation controller (CMAC) neural network was developed. A stepping motor and a PUMA 560 robot were used to study the feasibility of the developed technique. Experimental results have shown that the developed technique can analyze machine degradation quantitatively. This methodology could help operators set up machines for a given criterion, determine whether the machine is running correctly, and predict problems before they occur. As a result, maintenance hours could be used more effectively and productively.
Article
The Global Positioning System (GPS) is a satellite-based navigation and time transfer system developed by the U.S. Department of Defense. It serves marine, airborne, and terrestrial users, both military and civilian. Specifically, GPS includes the Standard Positioning Service (SPS) which provides civilian users with 100 meter accuracy, and it serves military users with the Precise Positioning Service (PPS) which provides 20-m accuracy. Both of these services are available worldwide with no requirement for a local reference station. In contrast, differential operation of GPS provides 2- to 10-m accuracy to users within 1000 km of a fixed GPS reference receiver. Finally, carrier phase comparisons can be used to provide centimeter accuracy to users within 10 km and potentially within 100 km of a reference receiver. This advanced tutorial will describe the GPS signals, the various measurements made by the GPS receivers, and estimate the achievable accuracies. It will not dwell on those aspects of GPS which are well known to those skilled in the radio communications art, such as spread-spectrum or code division multiple access. Rather, it will focus on topics which are more unique to radio navigation or GPS. These include code-carrier divergence, codeless tracking, carrier aiding, and narrow correlator spacing.
Software-in-the-loop development and experimental testing of a semi-active magnetorheological coupling for 4wd on demand vehicles
  • R Russo
  • M Terzo
  • F Timpone
R. Russo, M. Terzo, and F. Timpone, "Software-in-the-loop development and experimental testing of a semi-active magnetorheological coupling for 4wd on demand vehicles," in Proc Mini Conf Veh Sys Dyn Identif Anomalies, 2008, pp. 73-82.
Sensor for determining movements of vehicle relative to reference surface
  • M Becker
  • K Kirstaetter
M. Becker and K. Kirstaetter, "Sensor for determining movements of vehicle relative to reference surface," Germany Patent DE4 444 223A1, december 13, 1994. [Online]. Available: http://documents.allpatents. com/l/22235209/DE4444223A1