scholarly journals Estimation of Vehicle Longitudinal Speed Based on Improved Kalman Filter

2021 ◽  
Vol 2113 (1) ◽  
pp. 012011
Author(s):  
Qiang Zhang ◽  
Jun Xiao ◽  
Xiuhao Xi
Keyword(s):  
Dynamic Method ◽  
Longitudinal Velocity ◽  
Center Of Mass ◽  
State Estimator ◽  
Steering Angle ◽  
Safety Control ◽  
Estimation Algorithms ◽  
Driving Condition ◽  
Observation Noise ◽  
Vehicle Active Safety

Abstract Estimation of vehicle longitudinal acceleration is very important in vehicle active safety control system. In this paper, two driving conditions of a 4WD off-road vehicle are divided by vehicle signals such as steering angle. Under different working conditions, different estimation algorithms are adopted. In the straight driving condition, the longitudinal speed was estimated by adjusting the variance weight of acceleration Kalman observation noise based on kinematics method. For steering conditions, in order to obtain the longitudinal velocity at the center of mass, by dynamic method, a lateral state estimator was designed and tire sideslip dynamics was modeled. The CarSim-Simulink co-simulation results show that the proposed algorithm has high accuracy and strong practicability.

Vehicle Lateral and Longitudinal Velocity Estimation Using Coupled EKF and RLS Methods

2010 ◽  
Vol 29-32 ◽  
pp. 851-856 ◽  
Author(s):  
Liang Chu ◽  
Yong Sheng Zhang ◽  
Yan Ru Shi ◽  
Ming Fa Xu ◽  
Yang Ou
Keyword(s):  
Control Systems ◽  
Control Algorithm ◽  
Longitudinal Velocity ◽  
Velocity Estimation ◽  
Recursive Least Squares ◽  
Active Safety ◽  
Vehicle Model ◽  
Safety Control ◽  
The Cost ◽  
Vehicle Active Safety

In order to meet the cost requirement of lateral and longitudinal velocity measured directly in vehicle active safety control systems, based on 3-DOF vehicle model and the Recursive Least Squares (RLS) which can identify the tire cornering stiffness online, a control algorithm using Extended Kalman Filter(EKF) to estimate lateral and longitudinal velocity is proposed. The estimation values are compared with simulator values from CarSim. The compared results demonstrated that the proposed algorithm could estimate the lateral and longitudinal velocity accurately and robustly.

scholarly journals Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter

2020 ◽  
Vol 10 (4) ◽  
pp. 1343 ◽  
Author(s):  
Jianfeng Chen ◽  
Congcong Guo ◽  
Shulin Hu ◽  
Jiantian Sun ◽  
Reza Langari ◽  
...  
Keyword(s):  
Robust Estimation ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Gaussian White Noise ◽  
Active Safety ◽  
Safety Control ◽  
Guaranteed Estimation ◽  
Set Membership ◽  
Vehicle Motion ◽  
Vehicle Active Safety

Reliable vehicle motion states are critical for the precise control performed by vehicle active safety systems. This paper investigates a robust estimation strategy for vehicle motion states by feat of the application of the extended set-membership filter (ESMF). In this strategy, a system noise source is only limited as unknown but bounded, rather than the Gaussian white noise claimed in the stochastic filtering algorithms, such as the unscented Kalman filter (UKF). Moreover, as one part of this strategy, a calculation scheme with simple structure is proposed to acquire the longitudinal and lateral tire forces with acceptable accuracy. Numerical tests are carried out to verify the performance of the proposed strategy. The results indicate that as compared with the UKF-based one, it not only has higher accuracy, but also can provide a 100% hard boundary which contains the real values of the vehicle states, including the vehicle’s longitudinal velocity, lateral velocity, and sideslip angle. Therefore, the ESMF-based strategy can proffer a more guaranteed estimation with robustness for practical vehicle active safety control.

Monitoring of Human Driver Behavior by Vehicle Trajectory Reconstruction for Transportation Safety Management

2013 ◽  
Vol 300-301 ◽  
pp. 589-596 ◽  
Author(s):  
Geun Sub Heo ◽  
Sang Ryong Lee ◽  
Cheol Woo Park ◽  
Moon Kyu Kwak ◽  
Choon Young Lee
Keyword(s):  
Safety Management ◽  
Lateral Acceleration ◽  
Transportation Safety ◽  
Steering Wheel ◽  
Steering Angle ◽  
Safety Control ◽  
Acceleration Signal ◽  
Human Driver ◽  
Transportation Vehicle ◽  
Reckless Driving

In this paper, we proposed a method of monitoring human driver by reconstructing trajectories which transportation vehicle followed. For safety and management of logistic transportation, it is important to monitor the states of driving behavior through the whole course of path. Since many accidents occur due to the reckless driving of the driver every year, continuous monitoring of the status of commercial vehicles is needed for safety through the entire path from start point to the destination. To monitor the reckless driving, we tried to monitor the trajectory of the vehicle by using vehicle's lateral acceleration signal. Using the correlation between steering angle and lateral acceleration, we could find the relationship between steering angle and acceleration, and finally it is possible to estimate the global direction of vehicle maneuvering. We conducted experiments to find the history of vehicle position on the curved road using Kalman Filter, and classified steering wheel condition (over-steering, and under-steering). The method is applied to central safety management system for safety control of vehicles transporting toxic gases.

Estimation of Road Adhesion Coefficient Based on Tire Aligning Torque Distribution

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Biao Ma ◽  
Chen Lv ◽  
Yahui Liu ◽  
Minghui Zheng ◽  
Yiyong Yang ◽  
...  
Keyword(s):  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Estimation Method ◽  
Iteration Algorithm ◽  
Active Safety ◽  
Torque Distribution ◽  
Safety Control ◽  
Adhesion Coefficient ◽  
Road Friction ◽  
Vehicle Active Safety

Road adhesion coefficient is an important parameter in vehicle active safety control system. Many researchers estimate road adhesion coefficient by total tire self-aligning torque (SAT, also called front-axle aligning torque), which obtains the average road adhesion coefficient of front wheels, thus leading large estimation error. In this paper, a novel estimation of road adhesion coefficient based on single tire SAT, which is obtained by tire aligning torque distribution, is brought forward. Due to the use of SAT, the proposed estimation method is available in steering only condition. The main idea of the proposed method is that road adhesion coefficient is estimated by single tire SAT instead of total tire SAT. The single tire SAT is closer to real tire torque state, and it can be obtained by aligning torque distribution, which makes use of the ratio for the aligning torque of front-left wheel and front-right wheel. Tire sideslip angle used in torque distribution is estimated by unscented Kalman filter (UKF). Two coefficients, including front-left and front-right tire-road friction coefficients, are estimated by iteration algorithm form single tire SAT. The final road adhesion coefficient is determined by a coefficient identification rule, which is designed to determine which tire-road friction coefficient as the final road adhesion coefficient. Both simulations and tests that use gyroscope/lateral accelerometer/global position system (GPS)/strain gauge are conducted, to validate the proposed methodology that can provide accurate road adhesion coefficient to vehicle active safety control.

A review of vehicle active safety control methods: From antilock brakes to semiautonomy

2020 ◽  
pp. 107754632094865
Author(s):  
Arash Hosseinian Ahangarnejad ◽  
Ahmad Radmehr ◽  
Mehdi Ahmadian
Keyword(s):  
Active Safety ◽  
Traction Control ◽  
Real Time Processing ◽  
Safety Control ◽  
Potential Safety ◽  
Yaw Stability ◽  
Active Safety Systems ◽  
Safety Systems ◽  
Vehicle Active Safety ◽  
Antilock Brakes

A comprehensive review of technologies and approaches for active safety systems designed to reduce ground vehicle crashes, as well as the associated severity of injuries and fatalities, is provided. Active safety systems are commonly referred to as systems that can forewarn a driver of a potential safety hazard, or automatically intervene to reduce the likelihood of an accident without requiring driver intervention. The data from naturalistic drivers has shown that such systems are instrumental in improving vehicle safety in various conditions, particularly at higher speeds and under adverse road conditions. The increased integration of sensors, electronics, and real-time processing capabilities has served as one of the critical enabling elements in the widespread integration of active safety systems in modern vehicles. The emphasis is placed on control approaches for active safety systems and their progression over the years from antilock brakes to more advanced technologies that have nearly enabled semiautonomous driving. A review of key active safety control approaches for antilock braking, yaw stability, traction control, roll stability, and various collision avoidance systems is provided.

An Investigation on Steering Sensor Fault Reconstruction of Autonomous Driving for Functional Safety Using Sliding Mode Observer

2020 ◽  
Author(s):  
Kwang-Seok Oh ◽  
Kyong-Su Yi
Keyword(s):  
Sliding Mode ◽  
Longitudinal Velocity ◽  
Autonomous Driving ◽  
Sliding Mode Observer ◽  
Control Input ◽  
Functional Safety ◽  
Sensor Fault ◽  
Steering Angle ◽  
Fault Reconstruction ◽  
The Stability

Abstract This paper investigates on sensor fault reconstruction of sensors used for steering control of autonomous vehicle for functional safety. Sensor information such as steering angle and longitudinal velocity is generally needed for the design of steering feedback control system. If there exists unexpected fault signals in sensors, fatal accident can occur during autonomous driving because controller cannot compute the accurate control input. In this study, the sliding mode observer has been designed for fault reconstruction of steering angle and velocity sensors. In order to design the observer, the bicycle model that represents dynamic relationship between steering angle and velocities such as lateral velocity and yaw rate of vehicle has been used. The stability analysis has been conducted in accordance with velocity of the vehicle. The fault signals in sensors have been reconstructed using the injection term in sliding mode observer with the sliding mode gains designed for the stability. The performance evaluation has been conducted in Matlab/Simulink environment under the curved path tracking scenario.

AWD Vehicle Dynamics and Energy Efficiency Improvement by Means of Interaxle Driveline and Steering Active Fusion

2013 ◽  
Author(s):  
Vladimir V. Vantsevich
Keyword(s):  
Energy Efficiency ◽  
Power Distribution ◽  
Longitudinal Velocity ◽  
Lateral Force ◽  
Steering System ◽  
Lateral Acceleration ◽  
Mathematical Function ◽  
Steering Angle ◽  
Lateral Dynamics ◽  
Active Steering

This paper presents a novel approach to improve both energy efficiency and lateral dynamics of an all-wheel drive (AWD) vehicle by means of active functional/operational fusion of a driveline system, which distributes power between the front and rear driving axles, and a steering system that steers the front driving wheels. The paper starts by presenting the kinematic discrepancy factor, which is a normalized difference of the front and rear theoretical velocities that influences the wheel power distribution, as a mathematical function of the tire rolling radii in the driven mode, the gear ratios of the driveline system, and the steering angle of the front wheels. Using this function, the gear ratios from the transfer case to the front and rear wheels are determined to optimize vehicle energy efficiency by minimizing the kinematic discrepancy at the vehicle’s straight line motion and on a curve. It is also analytically shown that the wheel power distribution leads to the variation of the circumferential force of the front wheels that significantly influences the magnitude and direction of the front wheel lateral force. Thus, the paper introduced the wheel power distribution between the driving axles as an instrument for controlling oversteer-understeer transition of a vehicle, i.e., controlling vehicle lateral dynamics. Finally, the steering angle of the front wheels is considered and analyzed as an input of an active steering system to control the vehicle oversteer-understeer process in combination with the effect of the steering angle on the kinematic discrepancy factor. Longitudinal velocity control is added to constrain the lateral acceleration. Thus, the functional fusion of the active steering and driveline systems for enhancing both AWD vehicle energy efficiency and dynamics is introduced for the first time.

Novel, Subpixel Resolution Schemes for Particle Image Velocimeters

Volume 1 ◽  
2004 ◽  
Author(s):  
Michael R. Brady ◽  
Pavlos P. Vlachos
Keyword(s):  
Least Squares ◽  
Particle Image ◽  
Total Error ◽  
Center Of Mass ◽  
Particle Diameter ◽  
Least Square ◽  
Bias Error ◽  
Estimation Algorithms ◽  
New Methods ◽  
Least Squares Fit

Simultaneous velocity and size measurements are of great importance for resolving the dynamics of polydispersed multiphase flows. This effort explores novel sub-resolution particle center estimation algorithms for Digital Particle Image Velocimetry (DPIV) and Particle Tracking Velocimetry (DPTV). In addition these schemes provide direct measurements of the apparent particle image diameter using the same DPIV/DPTV recordings. Three novel fitting schemes were developed and compared with established methods such as center of mass, three-point Gaussian fit estimator and a least-square fit method. The new methods consist of two variations of a four point gaussian estimator and a variation of the conventional least-squares fit estimator. The new methods eliminate the bias error due to pixel discretization, thus significantly reducing the total error in the position and sizing measurement compared to the classic three point and least squares Gaussian estimators. In addition, the accuracy of the least-squares fits were essentially independent of the true particle diameter and significantly reduced the particle position error compared with current estimation schemes.

scholarly journals Vehicle State Estimation Based on Strong Tracking Central Different Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yingjie Liu ◽  
Qijiang Xu ◽  
Jingxia Sun ◽  
Fapeng Shen ◽  
Dawei Cui
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Vehicle Dynamics ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Tire Model ◽  
Dynamics Model ◽  
Safety Control ◽  
Vehicle State Estimation ◽  
Vehicle Active Safety

Vehicle active safety control was a key technology to avoid serious safety accidents, and accurate acquisition of vehicle states signals was a necessary prerequisite to achieve active vehicle safety control. Based on the purpose, a 3-DOF nonlinear vehicle dynamics model containing constant noise and a nonlinear tire model were established, and several vehicle key states were estimated by a strong tracking central different Kalman filter (CDKF). The conclusion showed that the proposed estimator had higher accuracy and less computation requirement than the CKF, CDKF, and UKF estimators. Numerical simulation and experiments indicated that the proposed vehicle state estimation method not only had higher estimation accuracy but also had higher real-time function.

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