Camera calibration and near-view vehicle speed estimation

2008 ◽  
Author(s):  
Futang Peng ◽  
Changsong Liu ◽  
Xiaoqing Ding
Keyword(s):  
Camera Calibration ◽  
Speed Estimation ◽  
Vehicle Speed

scholarly journals Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

2019 ◽  
Vol 11 (6) ◽  
pp. 123
Author(s):  
Huanan Dong ◽  
Ming Wen ◽  
Zhouwang Yang
Keyword(s):  
Optical Flow ◽  
Camera Calibration ◽  
Absolute Error ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Convolutional Network ◽  
Video Footage ◽  
Convolutional Networks ◽  
Calibration Methods ◽  
Non Local

Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial–temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km/h and mean square error (MSE) as 14.62 .

Acoustic vehicle speed estimation from single sensor measurements

2021 ◽  
pp. 1-1
Author(s):  
Slobodan Djukanovic ◽  
Jiri Matas ◽  
Tuomas Virtanen
Keyword(s):  
Speed Estimation ◽  
Vehicle Speed ◽  
Single Sensor

scholarly journals Cyber-Physical Attack Detection and Recovery based on RNN in Automotive Brake Systems

2018 ◽  
Author(s):  
Jongho Shin ◽  
Youngmi Baek ◽  
Jaeseong Lee ◽  
Seonghun Lee
Keyword(s):  
Classification Accuracy ◽  
Weighted Average ◽  
Data Integrity ◽  
Wheel Speed ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Detection And Identification ◽  
Fault Detection And Identification

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

A Vehicle Speed Estimation Algorithm Based on Dynamic Time Warping Approach

2017 ◽  
Vol 17 (8) ◽  
pp. 2456-2463 ◽  
Author(s):  
Zusheng Zhang ◽  
Tiezhu Zhao ◽  
Xin Ao ◽  
Huaqiang Yuan
Keyword(s):  
Dynamic Time Warping ◽  
Estimation Algorithm ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Time Warping ◽  
Dynamic Time
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