A New Vehicle Speed Estimation Method Based on the Longest Radial Crack on Windshield

2010 ◽  
Vol 34-35 ◽  
pp. 512-516
Author(s):  
Jun Xu ◽  
Meng Yi Zhu ◽  
Bo Han Liu ◽  
Yue Ting Sun ◽  
Yi Bing Li
Keyword(s):  
Real World ◽  
Unknown Parameter ◽  
Radial Crack ◽  
Estimation Method ◽  
Propagation Model ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Vehicle Accident ◽  
Good Agreement ◽  
Model Crack

Pedestrian-vehicle accident without road marks has long been a headache to accident investigators. This paper suggested a new method with the application of fracture mechanics to estimate impact speed in pedestrian-vehicle. Firstly, a windshield crack propagation model based on the crack initiation model put forward by Freund [1] is established. In the model, crack bluntness coefficient is an unknown parameter, depending on various factors, so speed domain is then divided into five intervals and sample real-world accident cases are employed to the calibrate crack bluntness coefficient in different speed intervals. Further, fourth-order Runge Kutta’s method is used to solve the differential equation. Five additional real-world accident cases are then employed to verify the accuracy of the model. Results show good agreement between the model results and the real impact speeds. Finally, the advantages and limitations of this method are discussed.

scholarly journals ACCURATE VEHICLE SPEED ESTIMATION FROM MONOCULAR CAMERA FOOTAGE

2020 ◽  
Vol V-2-2020 ◽  
pp. 419-426
Author(s):  
D. Bell ◽  
W. Xiao ◽  
P. James
Keyword(s):  
Real World ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Perspective Projection ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Reference Dataset ◽  
Deep Learning Algorithm ◽  
Sort Algorithm ◽  
Vehicle Platform

Abstract. A workflow is devised in this paper by which vehicle speeds are estimated semi-automatically via fixed DSLR camera. Deep learning algorithm YOLOv2 was used for vehicle detection, while Simple Online Realtime Tracking (SORT) algorithm enabled for tracking of vehicles. Perspective projection and scale factor were dealt with by remotely mapping corresponding image and real-world coordinates through a homography. The ensuing transformation of camera footage to British National Grid Coordinate System, allowed for the derivation of real-world distances on the planar road surface, and subsequent simultaneous vehicle speed estimations. As monitoring took place in a heavily urbanised environment, where vehicles frequently change speed, estimations were determined consecutively between frames. Speed estimations were validated against a reference dataset containing precise trajectories from a GNSS and IMU equipped vehicle platform. Estimations achieved an average root mean square error and mean absolute percentage error of 0.625 m/s and 20.922 % respectively. The robustness of the method was tested in a real-world context and environmental conditions.

A high accurate vehicle speed estimation method

2019 ◽  
Vol 24 (2) ◽  
pp. 1283-1291
Author(s):  
Shengnan Lu ◽  
Yuping Wang ◽  
Huansheng Song
Keyword(s):  
Estimation Method ◽  
Speed Estimation ◽  
Vehicle Speed

scholarly journals A Data-Driven Method for Vehicle Speed Estimation

2021 ◽  
Vol 23 (3) ◽  
pp. B165-B177
Author(s):  
Angelo Bonfitto ◽  
Stefano Feraco
Keyword(s):  
Neural Networks ◽  
Estimation Method ◽  
Mean Value ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
The Road ◽  
Computing Platform ◽  
Road Condition ◽  
The Mean

This paper presents a method based on Artificial Neural Networks for estimation of the vehicle speed. The technique exploits the combination of two tasks: a) speed estimation by means of regression neural networks dedicated to different road conditions (dry, wet and icy); b) identification of the road condition with a pattern recognition neural network. The training of the networks is conducted with experimental datasets recorded during the driving sessions performed with a vehicle on different tracks. The effectiveness of the proposed approach is validated experimentally on the same car by deploying the algorithm on a dSPACE computing platform. The estimation accuracy is evaluated by comparing the obtained results to the measurement of an optical sensor installed on the vehicle and to the output of another estimation method, based on the mean value of velocity of the four wheels.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

Speed estimation method for ultrahigh-speed induction machine utilizing rotor slot harmonics appearing in neutral point voltage

2011 ◽  
Vol 176 (3) ◽  
pp. 59-68
Author(s):  
Koichi Nishibata ◽  
Muneaki Ishida ◽  
Shinji Doki ◽  
Takashi Masuzawa ◽  
Masami Fujitsuna
Keyword(s):  
Induction Machine ◽  
Estimation Method ◽  
Neutral Point ◽  
Speed Estimation

ACOUSTIC THREE-DIMENSIONAL EFFECTS AROUND THE TAIWAN STRAIT: COMPUTATIONAL RESULTS

1999 ◽  
Vol 07 (01) ◽  
pp. 15-26 ◽  
Author(s):  
CHI-FANG CHEN ◽  
JANG-JIA LIN ◽  
DING LEE
Keyword(s):  
Field Data ◽  
Bottom Topography ◽  
Three Dimensional ◽  
Propagation Model ◽  
Computational Results ◽  
Submarine Canyons ◽  
Offshore Area ◽  
Ocean Environment ◽  
The Taiwan Strait ◽  
Good Agreement

A set of experiments were performed in the offshore area off the coasts of Taiwan and three-dimensional (3-D) measurements recorded. The 3-D effect on underwater propagation due to azimuthal variation of bottom topography is studied for the offshore regions southwest of Taiwan, where submarine canyons exist. A 3-D acoustic propagation model, FOR3D, is used to detect the 3-D effect. Computational results show that the 3-D effect is more prominent along the axis of the canyon than across it. Calculations show a very good agreement with field data, which indicate that the 3-D effect exists in this realistic ocean environment.

Sign in / Sign up

Export Citation Format

Share Document