Vehicle Yaw Rate Estimation Using a Virtual Sensor

Abstract

Road vehicle yaw stability control systems like electronic stability program (ESP) are important active safety systems used for maintaining lateral stability of the vehicle. Vehicle yaw rate is the key parameter that needs to be known by a yaw stability control system. In this paper, yaw rate is estimated using a virtual sensor which contains kinematic relations and a velocity-scheduled Kalman filter. Kinematic estimation is carried out using wheel speeds, dynamic tire radius, and front wheel steering angle. In addition, a velocity-scheduled Kalman filter utilizing the linearized single-track model of the road vehicle is used in the dynamic estimation part of the virtual sensor. The designed virtual sensor is successfully tested offline using a validated, high degrees of freedom, and high fidelity vehicle model and using hardware-in-the-loop simulations. Moreover, actual road testing is carried out and the estimated yaw rate from the virtual sensor is compared with the actual yaw rate obtained from the commercial yaw rate sensor to demonstrate the effectiveness of the virtual yaw rate sensor in practical use.

Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle

“…In [ 3 ], Kalman filters were applied to find the faulty sensors and approach good state estimation considering stochastic problems. In addition, a virtual sensor was designed in [ 4 ], which can be used to diagnose faults in a real sensor.…”

“…Although the current sensors are advanced, in real situations under long-term operation, there is always the possibility that the sensors may fail. In addition, these sensors are commercially accessible and employed in car-stability systems, but their price is an issue for manufacturers who are attempting to reduce costs [ 4 ]. In addition, the failed sensors will produce incorrect signals, which will then influence the control of the vehicle.…”

“…The common detection methods for complex systems are the model-based method, the signal-based method, and the knowledge-based method [ 6 ]. Among them, the model-based method is the most widely used method today, which is based on information from multiple sensors to build the analytical redundancy [ 2 , 3 , 4 , 7 , 8 , 9 , 10 ]. In the system, the sensors are related and will be executed in parallel to provide a group of features.…”

Analysis of Lateral Dynamic Stability of Small (B1 Class) Vehicles / Mažų (B1 Klasės) Automobilių Skersinio Dinaminio Stabilumo Analizė

“…yaw rate) įvertinimo metodika, naudojant virtualius jutiklius, aprašoma Emirler et al (2012). Aktyvaus saugumo sistemos, tokios kaip elektroninė stabilumo programa (ESP), yra svarbios ir naudojamos transporto priemonių šoniniam stabilumui palaikyti.…”

Sensor Fault Detection and Signal Restoration in Intelligent Vehicles

“…Although studies on fault detection are particularly important for the aircraft sector [8,9,10,11,12,13], as even minor aircraft faults can lead to serious accidents, the increasing interest in autonomous vehicles has inspired numerous investigations of fault detection in the automotive sector [14,15,16,17,18,19,20,21,22,23]. Na et al [24] applied residual sensitivity as a threshold for predicting the occurrence of vehicle sensor failure, while Emirler et al [25] employed a virtual sensor featuring a velocity-scheduled Kalman filter to characterize vehicle kinematics and estimate the yaw rate. Huang and Su [26] devised a model-based fault detection and isolation scheme considering disturbance and noise to diagnose single sensor faults in intelligent navigation systems, while analytical redundancy and nonlinear transformation were also used to generate residual values used to detect embedded sensors in intelligent vehicles [27].…”