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This paper presents an Artificial Neural Network based algorithm to estimate the sideslip angle of a vehicle in different road conditions. The method is based on two parallel tasks: (a) estimation of the sideslip angle in three road conditions, namely dry, wet and icy, by means of three separate regression networks, and (b) identification of the road condition with a pattern recognition classifier allowing to select the correct estimation output among the three regression network outputs. The regression and classification networks are trained by means of datasets recorded during driving sessions conducted on a high-performance instrumented vehicle in the three road conditions and with different maneuvers and driving cycles. The algorithm has been deployed on the same vehicle to test the performance on the real application. The results are presented in terms of comparison between the estimated angle and its direct measurement obtained by a high precision optical sensor installed onboard. The resulting accuracy of the method is about 98.6%. [ABSTRACT FROM AUTHOR]

This paper qualitatively and quantitatively reviews and compares three typical tyre–road friction coefficient estimation methods, which are the slip slope method, individual tyre force estimation method and extended Kalman filter method, and then presents a new cost-effective tyre–road friction coefficient estimation method. Based on the qualitative analysis and the numerical comparisons, it is found that all of the three typical methods can successfully estimate the tyre force and friction coefficient in most of the test conditions, but the estimation performance is compromised for some of the methods during different simulation scenarios. In addition, all of these three methods need global positioning system (GPS) to measure the absolute velocity of a vehicle. To overcome the above-mentioned problem, a novel cost-effective estimation method is proposed in this paper. This method requires only the inputs of wheel angular velocity, traction/brake torque and longitudinal acceleration, which are all easy to be measured using available sensors installed in passenger vehicles. By using this method, the vehicle absolute velocity and slip ratio can be estimated by an improved nonlinear observer without using GPS, and the friction force and tyre–road friction coefficient can be obtained from the estimated vehicle velocity and slip ratio. Simulations are used to validate the effectiveness of the proposed estimation method. [ABSTRACT FROM AUTHOR]

While digital map has been applied to many advanced driver assistance system applications, one critical attribute - road/lane curvature—is not available in the existing digital maps. Curvature derivation using splines is a well-known method in the computer graphics modelling community; however, it was also found that the direct application of the spline method is often not appropriate for real-time safety applications due to the resultant discontinuities in the curvature estimates and the lack of robustness against map data errors [CAMP, Enhanced digital mapping project final report, United States Department of Transportation, Washington, DC, 2004]. This paper presents a method to reconstruct road curvature attributes in real time using digital map data based on the proposed circle centre search and circle selection algorithms. Simulation and experimental results demonstrated that the proposed method can deliver curvature estimates that meet the desired accuracy. The method was also applied to the curve over-speed warning system in an on-going research project: Vehicle Infrastructure Integration—California. Some preliminary experimental results are also presented in this paper. [ABSTRACT FROM AUTHOR]

Brake systems in trucks are crucial for ensuring the safety of vehicles and passengers on the roadways. Most trucks in the USA are equipped with S-cam drum brake systems and they are sensitive to maintenance. Brake deficiencies such as leaks and out-of-adjustment of the pushrod are a major cause of accidents involving trucks. Leaks in the air brake systems drastically affect braking performance by decreasing the maximum attainable braking pressure and also increasing the time required to attain the same, thereby resulting in longer stopping distances. Out-of-adjustment of the pushrod leads to loss of braking torque even if no leaks are present in the air brake system. In this paper, we present a mathematical model for an air brake system in the presence of leaks, with a view towards developing a diagnostic system for the air brake system based on the models. Additionally, we present a scheme that estimates the severity of leak in terms of the mass flow rate of air leaking from the air brake system to the atmosphere. This scheme can be implemented using a simple look-up table. We also present a steady-state pushrod stroke estimation scheme, based on brake chamber pressure measurements in the absence of any leaks in the air brake system. [ABSTRACT FROM AUTHOR]

A traction control system (TCS) for two-wheel-drive vehicles can conveniently be realised by means of slip control. Such a TCS is modified in this paper in order to be applicable to four-wheel-drive vehicles and anti-lock braking systems, where slip information is not readily available. A reference vehicle model is used to estimate the vehicle velocity. The reference model is excited by a saw-tooth signal in order to adapt the slip for maximum tyre traction performance. The model-based TCS is made robust to vehicle modelling errors by extending it with (i) a superimposed loop of tyre static curve gradient control or (ii) a robust switching controller based on a bi-directional saw-tooth excitation signal. The proposed traction control strategies are verified by experiments and computer simulations. [ABSTRACT FROM AUTHOR]

This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms. [ABSTRACT FROM AUTHOR]

In this paper, considering the dynamical model of tyre-road contacts, we design a nonlinear observer for the on-line estimation of tyre-road friction force using the average lumped LuGre model without any simplification. The design is the extension of a previously offered observer to allow a muchmore realistic estimation by considering the effect of the rolling resistance and a term related to the relative velocity in the observer. Our aim is not to introduce a new friction model, but to present a more accurate nonlinear observer for the assumed model. We derive linear matrix equality conditions to obtain an observer gain with minimum pole mismatch for the desired observer error dynamic system. We prove the convergence of the observer for the non-simplified model. Finally, we compare the performance of the proposed observer with that of the previously mentioned nonlinear observer, which shows significant improvement in the accuracy of estimation. [ABSTRACT FROM AUTHOR]

Vehicle sideslip angle can be estimated using either dynamic or kinematic models. The dynamic model requires vehicle parameters, which might have uncertainties due to different load conditions, vehicle motions, and road frictions. Parameter uncertainties might result in estimation errors. Thus system identifications are required to estimate those parameters online. On the other hand, the kinematic model does not require these parameters. A closed-loop estimator can be formulated to estimate the sideslip angle using the kinematic model. Since the system matrix which consists of the yaw rate is time varying, the required input vector and output contain process and measurement noises, respectively, and the disturbance input matrix contains estimated states, extended Kalman filter is used to obtain the estimation gain in this paper. CarSim is used to evaluate the proposed approach under different driving scenarios and road frictions in Matlab/Simulink. The preliminary results show promising improvement of the sideslip angle estimation. [ABSTRACT FROM AUTHOR]