scholarly journals City Routing Velocity Estimation Model under theEnvironment of Lack of Floating Car Data

2012 ◽  
Vol 04 (01) ◽  
pp. 55-61 ◽  
Author(s):  
Chun Liu ◽  
Nan Li ◽  
Meixian Huang ◽  
Hangbin Wu
Keyword(s):  
Velocity Estimation ◽  
Estimation Model ◽  
Floating Car Data

The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data

2014 ◽  
Vol 694 ◽  
pp. 80-84
Author(s):  
Xiao Tong Yin ◽  
Chao Qun Ma ◽  
Liang Peng Qu
Keyword(s):  
Road Traffic ◽  
Velocity Estimation ◽  
Least Square ◽  
Single Type ◽  
Estimation Model ◽  
Average Speed ◽  
Urban Road ◽  
Traffic State ◽  
Floating Car Data ◽  
Different Types

The analysis of the unban road traffic state based on kinds of floating car data, is based on the model and algorithm of floating car data preprocessing and map matching, etc. Firstly, according to the characteristics of the different types of urban road, the urban road section division has been carried on the elaboration and optimization. And this paper introduces the method of calculating the section average speed with single floating car data, also applies the dynamic consolidation of sections to estimate the section average velocity.Then the minimum sample size of floating car data is studied, and section average velocity estimation model based on single type of floating car data in the different case of floating car data sample sizes has been built. Finally, the section average speed of floating car in different types is fitted to the section average car speed by the least square method, using section average speed as the judgment standard, the grade division standard of urban road traffic state is established to obtain the information of road traffic state.

Redatuming through a salt canopy and target-oriented salt-flank imaging

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. S63-S71 ◽  
Author(s):  
Rongrong Lu ◽  
Mark Willis ◽  
Xander Campman ◽  
Jonathan Ajo-Franklin ◽  
M. Nafi Toksöz
Keyword(s):  
Velocity Gradient ◽  
Model Building ◽  
Velocity Structure ◽  
Velocity Model ◽  
Seismic Profile ◽  
Velocity Estimation ◽  
Reverse Time ◽  
Estimation Model ◽  
Depth Migration ◽  
Prestack Migration

We describe a new shortcut strategy for imaging the sediments and salt edge around a salt flank through an overburden salt canopy. We tested its performance and capabilities on 2D synthetic acoustic seismic data from a Gulf of Mexico style model. We first redatumed surface shots, using seismic interferometry, from a walkaway vertical seismic profile survey as if the source and receiver pairs had been located in the borehole at the positions of the receivers. This process creates effective downhole shot gathers by completely moving surface shots through the salt canopy, without any knowledge of overburden velocity structure. After redatuming, we can apply multiple passes of prestack migration from the reference datum of the bore-hole. In our example, first-pass migration, using only a simple vertical velocity gradient model, reveals the outline of the salt edge. A second pass of reverse-time, prestack depth migration using full two-way wave equation was performed with an updated velocity model that consisted of the velocity gradient and salt dome. The second-pass migration brings out dipping sediments abutting the salt flank because these reflectors were illuminated by energy that bounced off the salt flank, forming prismatic reflections. In this target-oriented strategy, the computationally fast redatuming process eliminates the need for the traditional complex process of velocity estimation, model building, and iterative depth migration to remove effects of the salt canopy and surrounding overburden. This might allow this strategy to be used in the field in near real time.

scholarly journals An improved estimation method for vehicle velocity of distributed drive electric vehicle

2021 ◽  
Author(s):  
Qingqing Xiang ◽  
Zhiqiang Liu ◽  
Guang Liu
Keyword(s):  
Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Velocity Estimation ◽  
Estimation Model ◽  
Time Performance ◽  
Vehicle Velocity ◽  
Distributed Drive Electric Vehicle ◽  
Tracking Filter

Abstract In this paper, Simulink and Carsim are combined to study the velocity estimation of distributed drive electric vehicles. Firstly, the minimum co-simulation system is established to complete the design and debugging of the algorithm. Then, a new algorithm combining unscented Kalman filter and strong tracking filter is proposed based on the vehicle estimation model. The accuracy and real-time performance of the velocity estimation algorithm are validated by simulation under snake-shaped driving conditions with different road adhesion coefficients. Finally, an experimental test is carried out to verify the effectiveness of the proposed algorithm in estimating vehicle velocity.

Sensor Signal Limitations in Wheel Rotational Kinematics Estimation Model

2015 ◽  
Author(s):  
Jesse R. Paldan ◽  
Jeremy P. Gray ◽  
Vladimir V. Vantsevich
Keyword(s):  
Rotational Velocity ◽  
Rolling Resistance ◽  
Velocity Estimation ◽  
Sensor Signal ◽  
Estimation Model ◽  
Rotational Angle ◽  
Magnetic Components ◽  
Road Profile ◽  
Rotational Kinematics ◽  
Mechanical Elements

Wheel encoders play an important role in providing information about rotational kinematics of vehicle wheels. The sensor signals are utilized in critical vehicle systems responsible for vehicle safety, traction and braking performance, and stability of motion. This paper starts with an analysis of different types of sensors that have been used in rotational wheel kinematics estimations and controls. The main attention is given to sensor signal limitations related to the accuracy of measurement and response time that are important for agile-to-real-time tire dynamics estimation. A detailed analysis of the wheel rotational velocity estimation process is presented for a conventional Hall Effect digital sensor. Through an analytical modelling, it is shown that this sensor can limit its accuracy due to an increased time for signal information assembly caused by the number of impulses and transient (unsteady) rotational motion in unstable road conditions. A new concept of a rotational kinematics sensor is proposed and modeled as a multi-domain mechatronic system that includes new mechanical elements as well as electrical and magnetic components. The sensor concept provides a smooth continuous signal through the full rotational angle of the wheel and precise information about the rotational velocity and its changes in different unstable road conditions. Computational examples of both sensors (digital and proposed) are demonstrated with the use of a quarter-car model moving over a random road profile in stochastic gripping and rolling resistance conditions. A comparison of the two sensors’ accuracy to estimate the rotational velocity of the wheel is done with regard to an “ideal” sensor with a unity transfer function.

AVDM: Angular Velocity Decoding Model Accounting for Visually Guided Flight Behaviours of the Bee

2019 ◽  
Author(s):  
Huatian Wang ◽  
Qinbing Fu ◽  
Hongxin Wang ◽  
Paul Baxter ◽  
Jigen Peng ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Spatial Frequency ◽  
Visual Motion ◽  
Neural Mechanism ◽  
Velocity Estimation ◽  
Image Motion ◽  
Estimation Model ◽  
Neuron Spike ◽  
Terrain Following ◽  
Angular Velocity Estimation

AbstractWe present a new angular velocity estimation model for explaining the honeybee’s flight behaviours of tunnel centring and terrain following, capable of reproducing observations of the large independence to the spatial frequency and contrast of the gratings in visually guide flights of honeybees. The model combines both temporal and texture information to decode the angular velocity well. The angular velocity estimation of the model is little affected by the spatial frequency and contrast in synthetic grating experiments. The model is also tested behaviourally in Unity with the tunnel centring and terrain following paradigms. Together with the proposed angular velocity based control algorithms, the virtual bee navigates well in a patterned tunnel and can keep a certain distance from undulating ground with gratings in a series of controlled trials. The results coincide with both neuron spike recordings and behavioural path recordings of honeybees, demonstrating that the model can explain how visual motion is detected in the bee brain.Author summaryBoth behavioural and electro-physiological experiments indicate that honeybees can estimate the angular velocity of image motion in their retinas to control their flights, while the neural mechanism behind has not been fully understood. In this paper, we present a new model based on previous experiments and models aiming to reproduce similar behaviours as real honeybees in tunnel centring and terrain following simulations. The model shows a large spatial frequency independence which outperforms the previous model, and our model generally reproduces the wanted behaviours in simulations.

scholarly journals Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2904
Author(s):  
Anniek Eerdekens ◽  
Margot Deruyck ◽  
Jaron Fontaine ◽  
Bert Damiaans ◽  
Luc Martens ◽  
...  
Keyword(s):  
High Accuracy ◽  
Velocity Estimation ◽  
Accelerometer Data ◽  
Activity Detection ◽  
Estimation Model ◽  
User Friendliness ◽  
Training Activity ◽  
Fitness Level ◽  
Training Activities ◽  
First Time

Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.

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