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.

Lane Detection Algorithm Based on Genetic Algorithm and its Parallel Computing Realization

2012 ◽  
Vol 479-481 ◽  
pp. 65-70
Author(s):  
Xiao Hui Zhang ◽  
Liu Qing ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Data Transmission ◽  
High Speed ◽  
High Performance ◽  
Large Data ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Road ◽  
Time Problem ◽  
High Speed Data

Based on the target detection of alignment template, the paper designs a lane alignment template by using correlation matching method, and combines with genetic algorithm for template stochastic matching and optimization to realize the lane detection. In order to solve the real-time problem of lane detection algorithm based on genetic algorithm, this paper uses the high performance multi-core DSP chip TMS320C6474 as the core, combines with high-speed data transmission technology of Rapid10, realizes the hardware parallel processing of the lane detection algorithm. By Rapid10 bus, the data transmission speed between the DSP and the DSP can reach 3.125Gbps, it basically realizes transmission without delay, and thereby solves the high speed transmission of the large data quantity between processor. The experimental results show that, no matter the calculated lane line, or the running time is better than the single DSP and PC at the parallel C6474 platform. In addition, the road detection is accurate and reliable, and it has good robustness.

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

Low-Cost Smart Heads-Up Display Assist System for Automobiles

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Gautham G ◽  
Deepika Venkatesh ◽  
A. Kalaiselvi
Keyword(s):  
Fuel Economy ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Raspberry Pi ◽  
Worst Case ◽  
The Road ◽  
Traffic Conditions ◽  
Metropolitan Cities ◽  
Ingenious Method

In recent years, due to the increasing density of traffic every year, it is been a hassle for drivers in metropolitan cities to maintain lane and speeds on road. The drivers usually waste time and effort in idling their cars to maintain in traffic conditions. The drivers get easily frustrated when they tried to maintain the path because of the havoc created. Transportation Institute found that the odds of a crash(or near crash) more than doubled when the driver took his or her eyes off the road formore than two seconds. This tends to cause about 23% of accidents when not following their lane paths. In worst case the fuel economy often drops and tends to cause increase in pollution about 28% to 36% per vehicle annually. This corresponds to the wastage of fuel. Owing to this problem, we proposed an ingenious method by which the lane detection can be made affordable and applicable to existing automobiles. The proposed prototype of lane detection is carried over with a temporary autonomous bot which is interfaced with Raspberry pi processor, loaded with the lane detection algorithm. This prototype bot is made to get live video which is then processed by the algorithm. Also, the preliminary setups are carried over in such a way that it is easily implemented and accessible at low cost with better efficiency, providing a better impact on future automobiles.

Head motion coefficient-based algorithm for distracted driving detection

2019 ◽  
Vol 53 (2) ◽  
pp. 171-188 ◽  
Author(s):  
Kwok Tai Chui ◽  
Wadee Alhalabi ◽  
Ryan Wen Liu
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Video Stream ◽  
Detection Algorithm ◽  
Distracted Driving ◽  
Head Motion ◽  
Content Type ◽  
Cell Phone Use ◽  
The Road ◽  
Video Frames

PurposeConcentration is the key to safer driving. Ideally, drivers should focus mainly on front views and side mirrors. Typical distractions are eating, drinking, cell phone use, using and searching things in car as well as looking at something outside the car. In this paper, distracted driving detection algorithm is targeting on nine scenarios nodding, head shaking, moving the head 45° to upper left and back to position, moving the head 45° to lower left and back to position, moving the head 45° to upper right and back to position, moving the head 45° to lower right and back to position, moving the head upward and back to position, head dropping down and blinking as fundamental elements for distracted events. The purpose of this paper is preliminary study these scenarios for the ideal distraction detection, the exact type of distraction.Design/methodology/approachThe system consists of distraction detection module that processes video stream and compute motion coefficient to reinforce identification of distraction conditions of drivers. Motion coefficient of the video frames is computed which follows by the spike detection via statistical filtering.FindingsThe accuracy of head motion analyzer is given as 98.6 percent. With such satisfactory result, it is concluded that the distraction detection using light computation power algorithm is an appropriate direction and further work could be devoted on more scenarios as well as background light intensity and resolution of video frames.Originality/valueThe system aimed at detecting the distraction of the public transport driver. By providing instant response and timely warning, it can lower the road traffic accidents and casualties due to poor physical conditions. A low latency and lightweight head motion detector has been developed for online driver awareness monitoring.

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.

Available Lane Detection Based on Radon Transform

2014 ◽  
Vol 1046 ◽  
pp. 415-424
Author(s):  
Xi Xi Deng ◽  
Xiao Nian Wang ◽  
Jin Zhu
Keyword(s):  
Radon Transform ◽  
Autonomous Vehicle ◽  
Perspective Transformation ◽  
Lane Detection ◽  
Detection Problem ◽  
The Road ◽  
Texture Information

To solve lane detection problem in the system of autonomous vehicle, this paper proposes a method of texture segment based on perspective transformation. In this paper, firstly road images were captured through cameras installed on the vehicle, then make a perspective transform to road plane, so that the road and the non-road texture information effectively stand out. After calculation of the texture trend in the transformed image, radon transform can effectively distinguish between the road and the non-road area, and achieve the purpose of the texture regional segment. Experiments prove that this method can be used on the lane detection, which eliminate barriers and road borders effectively.

scholarly journals Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 315
Author(s):  
Yeongwon Lee ◽  
Byungyong You
Keyword(s):  
Object Tracking ◽  
Dynamic Behavior ◽  
Free Space ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Detection Algorithm ◽  
Location Information ◽  
Classification Result ◽  
Detection Algorithms ◽  
Previous Algorithm

In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.

scholarly journals Lane Detection Techniques using Image Processing

2021 ◽  
Vol 40 ◽  
pp. 03011
Author(s):  
Vighnesh Devane ◽  
Ganesh Sahane ◽  
Hritish Khairmode ◽  
Gaurav Datkhile
Keyword(s):  
Image Processing ◽  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Lane Detection ◽  
Time Curve ◽  
Detection Techniques ◽  
The Road ◽  
Detection Systems ◽  
Processing Techniques ◽  
Main Shortcoming

Lane detection is a developing technology that is implemented in vehicles to enable autonomous navigation. Most lane detection systems are designed for roads with proper structure relying on the existence of markings. The main shortcoming of these approaches is that they might give inaccurate results or not work at all in situations involving unclear markings or the absence of them. In this study one such approach for detecting lanes on an unmarked road is reviewed followed by an improved approach. Both the approaches are based on digital image processing techniques and purely work on vision or camera data. The main aim is to obtain a real time curve value to assist the driver/autonomous vehicle for taking required turns and not go off the road.

Layered Space-Time Multi-User Detection Based on Kalman Filter

2012 ◽  
Vol 220-223 ◽  
pp. 2012-2016
Author(s):  
Ling Ling Yang ◽  
Hong Xiang Shao ◽  
Yong Sheng Hu ◽  
Chao Guo
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Bit Error Rate ◽  
Detection Algorithm ◽  
Space Time ◽  
Cdma System ◽  
Space Time Coding ◽  
Channel Tracking ◽  
Time Coding ◽  
Simulation Results

This paper represents the theories of mathematical model of DS-CDMA system, principle of Kalman filter and layered space-time coding. Then, for the purpose of overcoming the disadvantages of traditional multi-user detection algorithm, the paper proposes the layered space-time multi-user detection algorithm based on kalman filter. Simulation results show the effectiveness of the proposed algorithm, such as the convergence speed is quickened, bit error rate is reduced greatly and channel tracking capability is improved significantly.

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