Yaw Rate Sensor Research Articles

AI-ACCIDENT DETECTION SYSTEM FOR FAST RESCUE ON FREEWAYS USING ON-BOARD CAR MOTION SENSORS FUSION

Continuous tracking and detecting position systems are significant services that can using in many applications such as detecting accident cars on Freeways. This paper introduces Artificial intelligent (AI) method to quick discover the accident and determine the position of car on Freeways that will reduce the rate of fatalities. Most tracking and detecting position systems are based on Global Navigation Satellite System (GNSS). But the GNSS signal in certain areas such as buildings and tunnel is unavailable which make a big problem for these systems. To solve this problem, most traditional methods are based on integrated GNSS with other navigation sensors such as accelerometers, gyroscopes, odometers and so on. But these integrated methods still high cost and need to long time processing. Today, most modern Vehicles are equipping with several On-board Car Motion Sensors (OVMS) such as Acceleration Sensors (AS), Wheel Velocity Sensor (WVS), Gravity Sensor (GS), Yaw Rate Sensor (YRS) and additional to GNSS that can improve the safety and also use to determine and detect the position of accident vehicle. This paper develops an approach AI-accident detection system based on integrating GNSS with On-board Vehicle Motion Sensors (OVMS) using Extended Kalman Filter (EKF) algorithm. During GPS outages, the three Acceleration Sensors (3-ASs) are processing to determine velocities and then position of vehicle on three x, y and z axes. While the YRS and GS sensors are using to determine velocities angles on three x, y and z axes, respectively. In same time the three WVS sensors are using to correct velocities errors of three acceleration sensors (3-ASs) until GNSS signal is return again. The proposed AI-accident detection system is tested in different scenarios during GPS outage signal. The results of proposed method can actually detect accident and determine the position of vehicle with high efficiency.

An adaptive cascade predictive control strategy for connected and automated vehicles

SummaryConnectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

Real-time measurement of road friction coefficient with intelligent tires

In recent years, various Advanced Driver Assistance Systems (ADAS) have been developed. Current ADAS generally use information obtained from wheel speed, acceleration, yaw rate sensors and so on. However, it is difficult to measure the road friction coefficient in real time with ordinary sensors, and vehicle motion control according to the road surface has not been realized. Therefore, the measurement of road surface friction coefficients is important to improve vehicle safety. Thus, this research is being conducted to develop intelligent tires that can measure the road surface friction coefficient. The authors have already proposed a method to measure the road surface friction coefficient from the strain generated on the bottom surface of the wheel. In this study, real-time measurement of the road surface friction coefficient using the proposed method was conducted on four wheels, and the validity of the measurement was verified by comparing the results with simulation results using CarSim.

2.5D vehicle odometry estimation

It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a yaw rate model that is already understood in the literature, this is augmented by fitting a quadratic to the incoming signals, enabling interpolation, extrapolation, and a finer integration of the vehicle position. It is shown, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods. Utilising sensors that return the change in height of vehicle reference points with changing suspension configurations, a planar model of the vehicle suspension is defined, thus augmenting the odometry model. An experimental framework and evaluations criteria is presented by which the goodness of the odometry is evaluated and compared with existing methods. This odometry model has been designed to support low-speed surround-view camera systems that are well-known. Thus, some application results that show a performance boost for viewing and computer vision applications using the proposed odometry are presented.

Информационно-управляющая система сбора и передачи информации для безэкипажного судна

The article considers the principles of constructing and functioning the data collection and transmission system for developing an information and control system of an unmanned vessel. A problem of the information and control system developing is suggested in two stages: choosing methods and means of transmitting data from sensors to the data collection system, choosing the method of data transfer to the data collection system, implementing the data transmission channels; organizing data collection and storage for use in the autopilot device, implementing systems for receiving and synchronizing data and their placement in the storage system. There has been illustrated a block diagram of the information and control system for collecting and transmitting data for an unmanned vessel, which consists of two subsystems: data collection and data transmission. Selecting the main components and proposals on the software and hardware application is carried out. The detailed characteristics of the microcontroller, digital compass, microelectromechanical sensors and chartplotter are given. In the course of implementing the data collection system, a Maretron digital magnetic compass, CRM100 and CRM200 microelectromechanical sensors, which implement a yaw rate sensor and inclinometer, and an analog steering sensor were connected to the debug board. The software for the information and control system is written in the programming language using the STM32CubeIDE application development tools. It has been stated that the data transfer between different levels of the program is carried out using standard means of the program code.

Identification of the ESP sensors condition during the vehicle service life

The paper presents the proposals of extension of the periodic tests of the selected ESP system sensors: angular velocity sensor and lateral acceleration sensor using a universal diagnostics tester and a plate stand (a wheel play detector unit). The idea of this approach is to evaluate the signals from the above sensors in terms of their amplitude and frequency in the case of known forcing at the plate stand. Knowledge of the amplitude and frequency of the plates excitation and the model of tested vehicle allows for predicting the response of vehicle. On this way the verification of sensors indications is possible. This article presents the flat model of a vehicle placed on the plate stand, simulation tests and the results of its validation for three different vehicles. The results of the investigation show that the wheelbase of vehicle has a significant impact on the steady-state vibration amplitude. This conclusion is important in the practical application of this method to test the vehicle yaw rate sensor in the ESP system.

Real-Time Vehicle Positioning and Mapping Using Graph Optimization.

In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning sensor measurements and can be optimized using nonlinear techniques. We model pose-graphs using measurements from a precise stereo camera-based visual odometry system, a robust odometry system using the in-vehicle velocity and yaw-rate sensor, and an automotive-grade GNSS receiver. Our evaluation is based on a dataset with 180 km of vehicle trajectories recorded in highway, urban, and rural areas, accompanied by postprocessed Real-Time Kinematic GNSS as ground truth. We compare the architecture’s performance with (i) vehicle odometry and GNSS fusion and (ii) stereo visual odometry, vehicle odometry, and GNSS fusion; for offline and real-time optimization strategies. The results exhibit a 20.86% reduction in the localization error’s standard deviation and a significant reduction in outliers when compared with automotive-grade GNSS receivers.

Multistage Estimators for the Distributed Drive Articulated Steering Vehicle

The distributed drive articulated steering vehicle (DDASV) has a broad application prospect in the field of special operations. It is essential to obtain accurate vehicle states for better effect of active control. DDASV dynamic model is presented. To improve robustness, an adaptive strong tracking algorithm is applied to the singular value decomposition unscented Kalman filter (SVDUKF). Divided by yaw rate sensors and the tire models, two multistage estimators are established for DDASVs. Stable steering condition is simulated to investigate the influence on the estimated accuracy about the sensors and tire models. The velocities and tire forces are the key parameters to be estimated. The performance of each estimator regarding the practicability and accuracy is compared. The results show that all estimators are practicable. However, the accuracy of the estimated velocities based on yaw rate sensors is better and the transient tire model can improve the accuracy of estimated lateral forces more effectively for the estimator established with yaw rate sensors.

Fault-tolerant control of steer-by-wire systems under voltage and current sensors faults

Steer-by-wire (SBW) systems benefit from several advantages including lower weight, volume, and cost compared to counterpart electric power steering systems. However, system reliability is impacted since SBW performance is highly dependent on correct operation of several voltage, current, speed, yaw rate, and position sensors. This paper presents a new method in SBW system reliability improvement by introducing a novel fault detection, isolation, and reconstruction (FDIR) control technique. The proposed FDIR method detects if any fault is observed on voltage or current sensors and identifies the faulty sensor location. Additionally the identified faulty sensor’s output is replaced with an estimated value to allow system continuous fault-ride through operation. The SBW system equipped with proposed FDIR technique provides fault-tolerance property against up to 3 faulty voltage or current sensors. Therefore, enhanced overall reliability is obtained at no further hardware costs associated with additional sensors redundancy solutions. The proposed technique is studied using system modelling and hardware prototyping, and its effectiveness is demonstrated using simulation and experimental results.

Localization accuracy improvement of autonomous vehicles using sensor fusion and extended Kalman filter

Advanced driver assistance systems and autonomous vehicles rely heavily on position information, therefore, enhancing localization algorithms is an actively researched field. Novel algorithms fuse the signals of common vehicle sensors, the inertial measurement unit and global positioning system. This paper presents a localization algorithm for vehicle position estimation that integrates vehicle sensors (steering angle encoder, wheel speed sensors and a yaw-rate sensor) and GPS signals. The estimation algorithm uses an extended Kalman filter designed for a simplified version of the single track model. The vehicle dynamics-based model only includes calculation of the lateral force and planar motion of the vehicle resulting in the minimal state-space model and filter algorithm. A TESIS veDYNA vehicle dynamics and MathWorks Simulink-based simulation environment was used in the development and validation process. The presented results include different low- and high-speed maneuvers as well as filter estimates of the position and heading of the vehicle.

인공지능 학습을 통한 제한된 센서 융합기반의 헤딩각 추정을 이용한 차량의 후진 자율 주행의 정확도 개선

This paper aims to improve the performance of the BDAS (Backward Driving Assist System) using a sensor fusion technique. The existing BDAS has a 1-m positional deviated error, which is based on the estimation of the vehicle’s heading and path from the steering angle sensor. To reduce the deviated error to less than 0.5 m, the use of rear-wheel sensors is proposed to estimate the heading angle through sensor fusion. After characterizing the steering-angle sensor, rear-wheel sensor, and yaw-rate sensor, data from different road conditions are collected. These data are learned by a neural network to generate appropriate heading angles. As a result of experiments conducted using an actual car, the backward driving error is reduced by half, within 0.5 m.

Sensor Technologies for Automobiles and Robots

AbstractThis paper reviews various sensor technologies with application in automobiles and robots. Several automotive sensors have been developed using the micro‐electromechanical systems (MEMS) technology. The technologies related to combustion pressure sensors, yaw rate sensors, acceleration sensors, and optical scanners are described here. The combustion pressure sensors are used for engine control, while the yaw rate sensors of quartz and silicon crystals and the acceleration sensors are applied for vehicle motion control. Optical scanners are developed for autonomous driving. Several automotive sensors have been significantly reduced in size and cost, and their performance has been improved dramatically by MEMS technology. The inertial force‐sensing system with yaw rate and acceleration sensors enabled the assistant and partner robots to stand on two legs, walk, or invert and move on two wheels. Because the robots are required to have a variety of sensors, a nerve‐net sensing system was developed. This sensing system has three layers: the host, relay, and sensor nodes. The sensor node is a special large‐scale integration called a ‘nerve‐net chip.’ A triaxial force tactile sensor integrated with the nerve‐net chip was developed as a 2.8‐mm square surface‐mountable chip. A force and thermal tactile sensor was also developed, which detected heat flows and discriminated the materials of contacted objects. The heat flow sensor was thinned with thin‐film technology. The sensor technologies will be used for eco cars and autonomous vehicles. Next‐generation robots are also supported by several automotive sensor technologies. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Performance evaluation on GNSS, wheel speed sensor, yaw rate sensor, and gravity sensor integrated positioning algorithm for automotive navigation system

The Global Navigation Satellite System (GNSS) positioning technique is widely used for the automotive navigation system since it can provide the stable and accurate position and velocity in the most road environments at an affordable price. However, the performance of GNSS positioning technique is degraded in certain areas, where GNSS signals are blocked by buildings and tunnel. To overcome this problem, the GNSS positioning technique should be integrated with dead reckoning (DR) sensors such as accelerometer, gyroscope, and odometer. Recently, the most passenger cars are equipped with the Advanced Driver Assistance System (ADAS) based on numerous sensors to improve safety and convenience in driving. Among sensors for the ADAS, vehicle dynamic sensors such as wheel speed sensor (WSS), yaw rate sensor (YRS), gravity sensor (GS) can be used for the DR algorithm since those sensors measure vehicle’s motions. Therefore, this paper evaluates the vehicle positioning algorithm that integrate the GNSS with a three-dimensional dead reckoning based on WSS, YRS, and GS. The vehicle positioning algorithm is implemented through the extended Kalman filter of a loosely-coupled mode. Performance was evaluated through tests carried out in real driving trajectory including various GNSS signals reception environments. It is found that the proposed algorithm can be an alternative solution to compensate the limitation of the GNSS positioning technique, without the use of a low-cost inertial measurement unit.

Low-Cost Precise Vehicle Localization Using Lane Endpoints and Road Signs for Highway Situations

In highways, lane markings are undoubtedly the most widely used landmarks for vehicle localization. However, they have a drawback in that they lack information on longitudinal position estimation and ego-lane identification. To alleviate this drawback, this paper presents a practical vehicle localization system for highways. The proposed system utilizes lane endpoints to enhance longitudinal position accuracy and road signs to improve the ego-lane identification accuracy. This system efficiently fuses the lane markings, lane endpoints and road signs along with a digital map and other low-cost sensors in a particle filter framework. Since it only uses low-cost sensors such as a monocular front-view camera, in-vehicle wheel speed and yaw rate sensors, as well as a low-end global positioning system (GPS), it is ready to mount on mass-produced vehicles. In the experiment, the proposed system was quantitatively evaluated using a dataset obtained while driving on 40 km stretch of highway, and outperformed previous approaches by showing a lateral position error of less than 0.12 m and a longitudinal position error of less than 0.25 m in terms of root mean square error (RMSE).

Vehicle dynamics estimation via augmented Extended Kalman Filtering

The response of active safety systems of modern cars strongly depends on the estimation accuracy in the key motion states of the vehicle. One common limitation of current systems is the lack of adaptability in the parameters of the vehicle model that are usually treated as time-invariant, although they are not exactly known or are subject to temporal changes. As a direct consequence, time invariant-parameter control systems may achieve sub-optimal performance and/or deteriorate according to the driving conditions.This paper presents a non-linear model-based observer for combined estimation of motion states and tyre cornering stiffness. It is based on common onboard sensors, that is a lateral acceleration and yaw rate sensor, and it works during normal vehicle manoeuvering. The identification framework relies on an augmented Extended Kalman filter to deal with model parameter variability and noisy measurement input. Results are described to evaluate the performance and sensitivity of the proposed approach, showing an improvement in the estimation accuracy that can reach an order of magnitude compared to standard approaches.

Robust lane tracking algorithm for forward target detection of automated driving vehicles

Detection of a primary forward target is one of the most important factors in a longitudinal control system of automated driving vehicles. If there is no special event such as a lane change, a lane detected by a vision sensor can be deemed as a predicted driving path of the ego vehicle. Based on this, it is possible to determine the state of the primary forward target vehicle accurately using the detected lanes. However, malfunction of vision sensors can induce unrecognized and misrecognized lane detection problems on account of internal/external environment factors. Furthermore, if a detection point is getting farther from the subject vehicle, the detection accuracy is getting lower because of inaccuracy of lane model parameters. To solve these problems, a novel method of lane tracking has been investigated. First, an integrated sensor module that combines a virtual sensor and a vision sensor has been developed. The virtual sensor is combined kinematic and dynamic vehicle model to be used in the full-speed range. Second, a lane estimator to improve lane detection accuracy at a long distance has been developed. The lane width in the public road can be assumed to be constant in the same road type. Based on this assumption, the clothoid parameters can be restored and consequently improve the lane detection accuracy. The performance of the proposed algorithm was verified by actual vehicle tests on public roads with manual driving and on proving ground with an automated driving system. The proposed algorithm has been compared with a conventional method which is based on in-vehicle yaw rate sensor. The test results have shown significant improvement of lane tracking performance over the conventional method.

Performance analysis of the GNSS / MEMS-IMU / on-Board vehicle sensor / magnetometer-based positioning system during GNSS signal blockage

ABSTRACTRecently, as a result of developments in microelectromechanical systems (MEMS) technology, various studies have been conducted to perform positioning by combining low-cost MEMS-based IMUs and the GNSS. The advantage of MEMS IMU is its low cost; however, its limitation is that the navigation error rapidly increases when disconnected from the GNSS. Therefore, precise positioning is difficult in tunnels or urban environments, where GNSS signals are unreliable. For this reason, additional sensors are needed. In this study, we intend to improve the accuracy of existing GNSS/IMU couplings using internal sensors and a magnetometer (MAG) attached to a vehicle.In this study, a positioning algorithm is developed based on the extended Kalman filter using on-board vehicle sensors and a MAG in addition to GNSS/IMU. A wheel speed sensor (WSS) and yaw rate sensor (YRS) were used as the on-board vehicle sensors. Experimental data were acquired and performance was analyzed. The results show that the GNSS/MEMS-IMU/WSS/YRS/MAG combination has the most stable positional accuracy, with a horizontal deviation of about 3.6 m observed in 10 zones of 30-second GNSS signal blockage. The performance was not significantly improved by adding the YRS; however, when the WSS and the MAG were used, the performance was greatly improved in the zones with GNSS signal blockage.

Virtual Diagnostic Sensors Design for an Automated Guided Vehicle

In recent years, Automated Guided Vehicles (AGVs) have been playing an increasingly important role in producing industry and infrastructure and will soon arrive to other areas of human life such as the transportation of goods and people. However, several challenges still aggravate the operation of AGVs, which limit the amount of implementation. One major challenge is the realization of reliable sensors that can capture the different aspects of the state of an AGV as well as its surroundings. One promising approach towards more reliable sensors is the supplementary application of virtual sensors, which are able to generate virtual measurements by using other sources of information such as actuator states and already existing sensors together with appropriate mathematical models. The focus of the research described in this paper is the design of virtual sensors determining forces and torques acting on an AGV. The proposed novel approach is using a quadratic boundedness approach, which makes it possible to include bounded disturbances acting on the AGV. One major advantage of the presented approach is that the use of complex tire models can be avoided. Information from acceleration and yaw rate sensors is processed in order to realize reliable virtual force and torque sensors. The resulting force and torque information can be used for several diagnostic purposes such as fault detection or fault prevention. The presented approach is explained and verified on the basis of an innovative design of an AGV. This innovative design addresses another major challenge for AGVs, which is the limited maneuvering possibilities of many AGV designs. The innovative design allows nearly unlimited maneuvering possibilities but requires reliable sensor data. The application of the approach in the AGV resulted in the insight that the generated estimates are consistent with the longitudinal forces and torques obtained by a proven reference model.

Design of diagnostic estimators for an automated guided vehicle

The paper deals with the design of virtual sensors of longitudinal forces as well as torques acting onto an automated guided vehicle. These virtual sensors are based on a measurement set associated with accelerations, velocities and yaw rate sensors mounted on the robot. Subsequently, the estimated forces and torques are used for diagnostic purposes concerning their appropriate distributions. The main novelty of the proposed approach is that it eliminates the necessity of incorporating wheel models, which usually impairs the performance of the approaches proposed in the literature. The final part of the paper presents experimental results, which clearly exhibit the performance of the proposed approach.

A Novel Method to Estimate Yaw Rate Based on Ensemble Empirical Mode Decomposition

Vehicle yaw rate is the key parameter that needs to be known by an Electronic Stability Control (ESC) system to improve the stability of the vehicle motion in emergency situations. In this paper, a novel method is proposed to estimate vehicle yaw rate which eliminates the need for the installation of conventional yaw rate sensor. In this method, yaw rate is roughly estimated by exploiting wheel speed, which is obtained from the preinstalled anti-lock braking system (ABS) wheel speed sensor. Then, a novel threshold filtering algorithm based on ensemble empirical mode decomposition (EEMD) is designed to de-noise the rough yaw rate in order to improve the accuracy of yaw rate estimation. To verify the feasibility and effectiveness of the proposed method, real road tests were performed. The results demonstrate that the proposed method has comparable performance with the gyroscope.