Vehicle Velocity Estimation Research Articles

Fuzzy Adaptive State Estimation of Distributed Drive Electric Vehicles with Random Missing Measurements and Unknown Process Noise

Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles. However, as these states are not directly measured, Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them. Unfortunately, existing methods tend to ignore the impact of data loss on estimation performance. Furthermore, the process noise, which changes dynamically due to varying driving conditions, is not adequately considered. In response to these constraints, we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter (FAFTEKF). Initially, a fault-tolerant EKF is devised to handle missing measurements. Additionally, a fuzzy logic system that dynamically updates the process noise matrix, is built to improve estimation accuracy under different driving conditions. Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.

Absolute velocity estimation of UAVs based on phase correlation and monocular vision in unknown GNSS‐denied environments

AbstractThis paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios.

A framework for real-time vehicle counting and velocity estimation using deep learning

To better control traffic and promote environmental sustainability, this study proposed a framework to monitor vehicle number and velocity at real time. First, You Only Look Once-v4 (Yolo-v4) algorithm based on deep learning can greatly improve the accuracy of object detection in an image, and trackers, including Sort and Deepsort, resolved the identity switch problem to track efficiently the multiple objects. To that end, this study combined Yolo-v4 with Sort and Deepsort to develop two trajectory models, which are known as YS and YDS, respectively. In addition, different regions of interest (ROI) with different pixel distances (PDs), named ROI-10 and ROI-14, were converted by road marking to calibrate the PD. Finally, a high-resolution benchmark video and two real-time low-resolution videos of highway both were employed to validate this proposed framework. Results show the YDS with ROI-10 achieved 90% accuracy of vehicle counting, when compared to the number of actual vehicles, and this outperformed the YS with ROI-10. However, the YDS with ROI-14 generated relatively good estimates of vehicle velocity. As shown in the real-time low-resolution videos, the YDS with ROI-10 achieved 89.5% and 83.7% accuracy of vehicle counting in Nantun and Daya sites of highway, respectively, and reasonable estimates of vehicle velocity were obtained. In the future, more bus and light truck images could be collected to effectively train the Yolo-v4 and improve the detection of bus and light truck. A better mechanism for precise vehicle velocity estimation and the vehicle detection in different environment conditions should be further investigated.

Vehicle velocity estimation based on WSS/IMU with wheel slip recognition

Accurate velocity is of great significance to vehicle dynamic control, and vehicle velocity estimation based on wheel speed sensor (WSS) and inertial measurement unit (IMU) is more cost-effective than other sensors. Unavoidable wheel slip will result in velocity vector estimation error. To solve this problem, this paper proposed a novel wheel slip recognition criterion when using WSS/IMU for velocity estimation. The basic idea is to deduce the strict relationship between wheel speed, angular rate, and specific force when the wheel does not slip, and the slipping wheel will destroy this balance, which can be used to determine whether the wheel is slipping. Wheels identified as slipping are isolated from fusion, and only non-slipping wheels can be used for velocity estimation. In addition, the analysis shows that the wheel slip recognition is robust and works well even if there is an estimation error in the lateral velocity during vehicle turning. The co-simulation based on Carsim-Simulink shows that the proposed wheel slip recognition can ensure accurate vehicle velocity estimation at various working conditions. The real vehicle test further verified the important role of wheel slip recognition for velocity estimation.

Improve the accuracy of vehicle velocity estimation based on low-cost GPS/IMU by GPS course angle

Vehicle velocity is an important parameter for vehicle dynamics control. Global Positioning System (GPS) and inertial measurement unit (IMU) information can be used for velocity estimation by taking advantage of each other through fusion. However, if GPS velocity is used as the only measurement like classic GPS/INS, it will cause a large velocity estimation error or even divergence. This paper, from the perspective of system observability, proves that the GPS course angle (GCA) plays an important role in vehicle velocity estimation based on low-cost GPS/IMU. Firstly, construct the velocity estimation algorithm in the vehicle coordinate system directly. Then, according to the characteristics of different working conditions, the observability of the system without GCA is fully analyzed from the Lie derivative-based observability proof and the observability basic definition. The results show that the weak observability of the yaw angle is the most serious threat in vehicle velocity estimation. After fusing the GCA, the yaw angle potential divergence can be suppressed and the estimation accuracy of velocity will be improved. Even though roll and pitch are still unobservable under some conditions after augmenting GCA, the error propagation analysis shows that their estimation errors have little effect on velocity estimation, so the accurate estimation of velocity can be guaranteed. Finally, the indispensable of GCA in velocity estimation based on low-cost GPS/IMU is verified by Carsim-Simulink joint simulations and vehicle tests.

Airspeed-Aided State Estimation Algorithm of Small Fixed-Wing UAVs in GNSS-Denied Environments.

Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments.

In-water and in-air vehicle velocity estimation via harmonic and Doppler analysis

Remotely-controlled (RC) vehicles, such as RC cars, boats, planes, and drones, use high energy-density lithium polymer batteries that enable powerful brushless DC motors to propel them at remarkable velocities. In prior work, measurements of the acoustic emissions from such motors on RC cars have been processed to estimate vehicle velocity, based on a spectral analysis of the emissions, together with a parametric model for the acoustic emissions, relating them to motor speed and vehicle velocity. This work builds on prior models for the acoustic emissions of the DC motors to estimate the motor speeds for in-water and in-air craft, including RC boats and drones. Spectrograms of the acoustic recordings of the vehicles at moving at constant velocity provide sufficient harmonic structure to effectively measure the Doppler shift at closest point of proximity, enabling vehicle velocity estimates. These, in turn, enable calibration of the harmonic structure for motor speed estimation. Preliminary results demonstrate the correlation between the speed profile of the vehicle, acoustic harmonic structure, and Doppler shift.

Tracked Vehicle Velocity Estimation by Disturbance Observer and Machine Learning, and its Application to Driving Force Control for Slippage Suppression

Tracked Vehicle Velocity Estimation by Disturbance Observer and Machine Learning, and its Application to Driving Force Control for Slippage Suppression

A control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles

This paper presents a control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles. A model-based estimator for brake pressure estimation has been developed. The braking torque applied to the wheel has been computed using the estimated brake pressure for the control of the wheel slip both in braking and traction situations. The vehicle velocity and wheel slip ratio estimation algorithms have been designed using only wheel speed sensors. The proposed slip regulation algorithm has also been successfully implemented for the antilock braking system (ABS) and traction control system (TCS). In ABS, the slip ratio and wheel acceleration are stabilized by a limit cycle control of the braking pressure. The TCS has been implemented by combining engine torque control and pneumatic brake pressure control. The brake controller is based on the valve switched control that incorporates the wheel dynamics and valve on/off characteristics. The ABS and TCS algorithms are integrated into the slip regulation algorithm to reduce the computation load of an Electrical Control Unit (ECU). Four-wheel independent slip monitoring and slip ratio control algorithms have been implemented on the ECU, and their performance has been investigated via both computer simulations and vehicle tests. Both results show that the proposed algorithms enhance the acceleration and braking performance without vehicle acceleration information. Moreover, the proposed split-mu strategy has improved the lateral stability during braking, and the acceleration performance during accelerating on the split-mu road. It has been shown via vehicle tests that, compared to the reference commercial algorithm, the braking distance was reduced by more than 4% on the split-mu and low-mu roads, and the acceleration performance was improved by 7.9% on the split-mu road.

Adaptive Estimation of Vehicle Velocity From Updated Dynamic Model for Control of Anti-Lock Braking System

The main challenge for designing the automotive control systems like the anti-lock braking system (ABS) is to access a reliable model because of uncertainties and variations in vehicle dynamics and tire forces. In this article, the initial dynamic model of the vehicle braking system is updated at each instant by estimation of model compensatory terms using a novel prediction approach. The proposed estimation method uses the global navigation satellite system (GNSS) as a reference to adaptively estimate the vehicle velocity with high frequency in the presence of variable bias of acceleration sensor. The proposed estimation algorithm is mathematically analyzed and experimentally evaluated to show its high accuracy obtained by the adaptive tuning of parameters. Accordingly, a nonlinear controller based on the estimated dynamic model is designed to prevent the tire from being locked by tracking the desired slip. The designed control system is tested within the CarSim software as a virtual vehicle experiment platform. The obtained results reveal that the designed model-based control system remarkably improves the braking performance compared with the conventional sensor-based ABS controllers.

Passive velocity estimation of remotely-controlled vehicles via Doppler tracking

Passive velocity estimation of remotely-controlled vehicles via Doppler tracking

Cellular automata model for Urban Road traffic flow Considering Internet of Vehicles and emergency vehicles

Based on the forecast of the future prospect of the car network function, the paper analyzes the traffic characteristics of the future networked vehicles and the traffic characteristics of the emergency vehicles in the network environment. Then in order to better investigate the characteristics of Internet of Vehicles, MCD model (into which introduced the brake light) is improved by considering the vehicle safe distance which is depending on the driver’s reaction time and the estimation of the front vehicle velocity. Based on the improved model, the rules such as the speed perception of the connected vehicle, low reaction time of networked vehicle and the regional avoidance of the emergency vehicle are introduced, which constitute the emergency vehicle CA model of urban road traffic flow. The results show that the vehicle network can improve the traffic capacity and average speed of the road so that it has a good effect on the urban traffic, which is conducive to the rapid passage of emergency vehicles.

Alternative approaches for UAV dead reckoning based on the immunity paradigm

The immunity paradigm has been recently investigated as a potential solution to the problem of unmanned aerial vehicles (UAV) navigation when external sensor systems and information of opportunity are not available. The effectiveness of specialized immunity cells in memorizing the characteristics of invading antigens, as well as the characteristics of the response of the host organism, or the antibodies, represent the source of inspiration for the development of an on-board intelligent system, an artificial immune system (AIS). The AIS is expected to provide corrections to a simple dead reckoning algorithm, such that autonomous trajectory tracking control of a UAV can be maintained over extended periods of time. Variables that can characterize the dynamic state of the vehicle and can be measured exclusively with on-board sensors are assimilated to the antigens, while the antibodies consist of the adjustments applied to the integration algorithm, such that accurate estimations of vehicle position and velocity are obtained. The AIS is built as a collection of instantaneous values of antigens and corresponding antibodies, referred to as artificial memory cells. The AIS is constructed using data collected under nominal conditions, when all sensors and systems function correctly and vehicle position and velocity are available. Two alternative approaches for building and operating the AIS are implemented and investigated in this paper. One relies on providing corrections to the output and the other to the input of the integration scheme. The AIS for both scenarios have been generated and tested using the West Virginia University unmanned aerial systems simulation environment. Both alternative approaches have been demonstrated to achieve similar performance in terms of significantly improving the vehicle position and velocity estimation algorithm and leading to desirable trajectory tracking and mission completion.

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

SummaryVehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in‐wheel‐motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady‐state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single‐chip microcomputer verifies the strong real‐time performance and easy‐to‐implement characteristics of the proposed algorithm.

Research on Lane a Compensation Method Based on Multi-Sensor Fusion.

The curvature of the lane output by the vision sensor caused by shadows, changes in lighting and line breaking jumps over in a period of time, which leads to serious problems for unmanned driving control. It is particularly important to predict or compensate the real lane in real-time during sensor jumps. This paper presents a lane compensation method based on multi-sensor fusion of global positioning system (GPS), inertial measurement unit (IMU) and vision sensors. In order to compensate the lane, the cubic polynomial function of the longitudinal distance is selected as the lane model. In this method, a Kalman filter is used to estimate vehicle velocity and yaw angle by GPS and IMU measurements, and a vehicle kinematics model is established to describe vehicle motion. It uses the geometric relationship between vehicle and relative lane motion at the current moment to solve the coefficient of the lane polynomial at the next moment. The simulation and vehicle test results show that the prediction information can compensate for the failure of the vision sensor, and has good real-time, robustness and accuracy.

Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales

Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors. A practical estimation method for the longitudinal and lateral velocities of electric vehicles is proposed. Two reliable driving empirical judgements about the velocities are extracted from the signals of the ordinary onboard vehicle sensors, which correct the integral errors of the corresponding kinematic equations on a long timescale. Meanwhile, the additive biases of the measured accelerations are estimated recursively by comparing the integral of the measured accelerations with the difference of the estimated velocities between the adjacent strong empirical correction instants, which further compensates the kinematic integral error on short timescale. The algorithm is verified by both the CarSim-Simulink co-simulation and the controller-in-the-loop test under the CarMaker-RoadBox environment. The results show that the velocities can be accurately and reliably estimated under a wide range of driving conditions without prior knowledge of the tire-model and other unavailable signals or frequently changeable model parameters. The relative estimation error of the longitudinal velocity and the absolute estimation error of the lateral velocity are kept within 2% and 0.5 km/h, respectively.

Observer-based estimation of velocity and tire-road friction coefficient for vehicle control systems

A novel nonlinear observer for the estimation of vehicle velocity together with the tire-road friction coefficient is presented in this paper. The modular observer is designed based on a longitudinal tire force estimation approach and a lateral tire friction model. Compared to the state-of-art methods, the proposed observer design provides accurate estimation of the longitudinal velocity, lateral velocity, and the tire-road friction coefficient simultaneously. Particularly, the longitudinal tire forces are first estimated based on a filter observer. Then, according to the calculation of lateral tire forces, the nonlinear observer is proposed to estimate vehicle velocity and the tire-road friction coefficient. Moreover, the stability property of the observer is analyzed using a Lyapunov-based method. Simulation results validate the effectiveness of the proposed method.

Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles

When a low-cost micro-electro-mechanical system inertial measurement unit is used for a vehicle navigation system, errors will quickly accumulate because of the large micro-electro-mechanical system sensor measurement noise. To solve this problem, an automotive sensor–aided low-cost inertial navigation system is proposed in this article. The error-state model of the strapdown inertial navigation system has been derived, and the measurements from the wheel speed sensor and steer angle sensor are used as the new observation vector. Then, the micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated system is established based on adaptive Kalman filtering. The experimental results show that the positioning error of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor is 94.67%, 98.88%, and 97.88% less than the values using pure strapdown inertial navigation system in the east, north, and down directions, respectively. The yaw angle error is reduced to less than 1°, and the vehicle velocity estimation of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated navigation system is closer to the reference value. These results show the precision of the integrated navigation solution.

Modular scheme for four‐wheel‐drive electric vehicle tire‐road force and velocity estimation

The limited availability of vehicle state information, including tire-road forces and vehicle velocities, restricts the development of control strategies for intelligent vehicles and new energy vehicles. This study proposes a modular estimation scheme for tire-road forces and vehicle velocities that can effectively cope with the cyclic coupling of the vehicle dynamics. The longitudinal tire-road forces are estimated using a sliding mode observer. Then, an observer for the lateral tire-road forces that exist in a cascade structure with the longitudinal tire-road force observer is designed. Non-linear vehicle velocity observers that take the estimated longitudinal and lateral tire-road forces as inputs are designed. A genetic algorithm approach is employed to select the observer gains. Finally, experimental validations under normal conditions and offline simulations under critical conditions for verifying the robustness with respect to measurement noise are conducted. The results demonstrate that the proposed modular scheme for tire-road force and vehicle velocity estimation yields acceptable results and has potential value for real vehicle applications.

Non-linear tyre model–based non-singular terminal sliding mode observer for vehicle velocity and side-slip angle estimation

Vehicle velocity and side-slip angle are important vehicle states for the electronic stability programme and traction control system in vehicle safety control system and for the control allocation method of electric vehicles with in-wheel motors. This paper proposes an innovative side-slip angle estimator based on the non-linear Dugoff tyre model and non-singular terminal sliding mode observer. The proposed estimation method based on the non-linear tyre model can accurately present the tyre’s non-linear characteristics and can show advantages over estimation methods based on the linear tyre model. The utilised Dugoff tyre model has a relatively simple structure with few parameters, and the proposed non-linear observer can be applied in various vehicle tyres and various road conditions. Precise determination of the Dugoff tyre model parameters is not required and the proposed observer can still perform good estimation results even though tyre parameters and the tyre–road friction coefficient are not accurate. The proposed non-singular terminal sliding mode observer can achieve fast convergence rate and better estimation performance than the traditional sliding mode observer. At the end of this paper, simulations in various conditions are presented to validate the proposed non-linear estimator.