scholarly journals Lane Detection Algorithm Based on Road Structure and Extended Kalman Filter

2020 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Jinsheng Xiao ◽  
Wenxin Xiong ◽  
Yuan Yao ◽  
Liang Li ◽  
Reinhard Klette
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Strong Light ◽  
Edge Strength ◽  
Straight Line ◽  
Edge Points ◽  
The Stability

Lane detection still demonstrates low accuracy and missing robustness when recorded markings are interrupted by strong light or shadows or missing marking. This article proposes a new algorithm using a model of road structure and an extended Kalman filter. The region of interest is set according to the vanishing point. First, an edge-detection operator is used to scan horizontal pixels and calculate edge-strength values. The corresponding straight line is detected by line parameters voted by edge points. From the edge points and lane mark candidates extracted above, and other constraints, these points are treated as the potential lane boundary. Finally, the lane parameters are estimated using the coordinates of the lane boundary points. They are updated by an extended Kalman filter to ensure the stability and robustness. Results indicate that the proposed algorithm is robust for challenging road scenes with low computational complexity.

scholarly journals Extended Kalman Filter for Bearings-Only Tracking

2019 ◽  
Vol 8 (6) ◽  
pp. 637-640 ◽  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Constant Velocity ◽  
Target Motion ◽  
Passive Mode ◽  
Crucial Issue ◽  
Straight Line ◽  
Optimal Linear ◽  
Target Motion Analysis ◽  
Line Path

Target tracking using bearings-only measurements in passive mode operation of sonar is a crucial issue of underwater tracking. Target motion in underwater scenario is analyzed using bearings-only measurements and calculating parameters like range, course and speed of the target. This is called Target Motion Analysis (TMA). TMA process is highly non-linear as the measurements chosen are nonlinearly related to the selected target state vector and the traditional, optimal linear Kalman filter will not be appropriate to use. It is presumed that the target is moving in straight line path with constant velocity, so Extended Kalman Filter (EKF) is proposed in this paper. The algorithm is simulated for several scenarios using MATLAB. Monte-Carlo runs are performed to evaluate the capability of the algorithm.

Algorithm of Lane Detection on an Appropriate Limited Region

2013 ◽  
Vol 433-435 ◽  
pp. 267-272
Author(s):  
Xing Ma ◽  
Chun Yang Mu ◽  
Chun Tao Zhang ◽  
Lu Ming Zhang
Keyword(s):  
Urban Environment ◽  
Hough Transform ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Limited Region ◽  
Feature Based

This paper proposed a lane detection algorithm for urban environment. The algorithm was concerned on selecting an appropriate limited region of interest (ROI) by OTSU segmentation. Then candidates of lane markers were extracted by Canny, finally the lane boundaries were detected by Hough transform. The limited ROI helps to identification lane in an appropriate region. This process have the effect of enhancement in the speed of operation. The proposed algorithm was simulated in MATLAB. The test databases were shared by Fondazione Bruno Kessler (FBK). The experiments show that lane boundaries can be detected correctly although they are fade. Feature-based method is usually affected by intension of image. Several characteristics of roads need to be considered further for detection more precisely.

scholarly journals Traffic sign Detection using CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 129-135
Author(s):  
K. Mirunalini ◽  
Vasantha Kalyani David
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Traffic Sign ◽  
Straight Line ◽  
Essential Components ◽  
Sign Detection ◽  
Important Challenge ◽  
Bad Weather ◽  
Traffic Sign Detection

Lane Detection and Traffic sign detection are the essential components in ADAS .Although there has been significant quantity of analysis dedicated to the detection of lane detection and sign detection in the past, there is still need robustness in the system. An important challenge in the current algorithm is to cope with the bad weather and illumination. In this paper proposes an improved Hough transform algorithm in order to achieve detection of straight line while for the detection of curved sections, the tracking algorithm is studied. The proposed method uses Hybrid KSVD for removing the noise and Hybrid Lane Detection Algorithm is used for identifying the lanes and CNN based approach is used for the Traffic sign Detection. The proposed method offers better Peak Signal to Noise Ratio (PSNR) and Root Mean Square (RMS) in contrast to the existing methods.

Lane Detection Based on Road Module and Extended Kalman Filter

2018 ◽  
pp. 382-395 ◽  
Author(s):  
Jinsheng Xiao ◽  
Li Luo ◽  
Yuan Yao ◽  
Wentao Zou ◽  
Reinhard Klette
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lane Detection

scholarly journals Highly Curved Lane Detection Algorithms Based on Kalman Filter

2020 ◽  
Vol 10 (7) ◽  
pp. 2372
Author(s):  
Byambaa Dorj ◽  
Sabir Hossain ◽  
Deok-Jin Lee
Keyword(s):  
Kalman Filter ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Self ◽  
Full Curve ◽  
The Road ◽  
Detection Algorithms ◽  
Simulation Results

The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.

scholarly journals Control of Optoelectronic Scanning and Tracking Seeker by Means the LQR Modified Method with the Input Signal Estimated Using of the Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3109
Author(s):  
Daniel Gapiński ◽  
Zbigniew Koruba
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
Graphical Form ◽  
Linear Quadratic ◽  
Optoelectronic System ◽  
Modified Method ◽  
Maneuvering Targets ◽  
The Stability

The paper presents the concept of controlling the designed optoelectronic scanning and tracking seeker. The above device is intended for the so-called passive guidance of short-range anti-aircraft missiles to various types of air maneuvering targets. In the presented control method, the modified linear-quadratic regulator (LQR) and the estimation of input signals using the extended Kalman filter (EKF) were used. The LQR regulation utilizes linearization of the mathematical model of the above-mentioned seeker by means of the so-called Jacobians. What is more, in order to improve the stability of the seeker control, vector selection of signals received by the optoelectronic system was used, which also utilized EKF. The results of the research are presented in a graphical form. Numerical simulations were carried out on the basis of the author’s own program developed in the programming language C++.

scholarly journals A Constrained Neural Network Kalman Filter for Price Estimation in High Frequency Financial Data

1997 ◽  
Vol 08 (04) ◽  
pp. 399-415 ◽  
Author(s):  
Peter J. Bolland ◽  
Jerome T. Connor
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Financial Time Series ◽  
Financial Data ◽  
The Neural Network ◽  
Tick Data ◽  
Price Estimation ◽  
Network Weight ◽  
The Stability

In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly observed in financial data: process noise, measurement noise, and arrival noise. The erratic arrival of data (arrival noise) results in the neural network predictions being iterated into the future. Constraining the neural network to have a fixed point at the origin produces better iterated predictions and more stable results. The performance of constrained and unconstrained neural networks within the extended Kalman filter is demonstrated on "Quote" tick data from the $/DM exchange rate (1993–1995).

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