scholarly journals Robust lane detection algorithm based on triangular lane model

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1639-1647
Author(s):  
Young Park ◽  
Sung Na ◽  
Qun Wei ◽  
Ki Seong ◽  
Jyung Lee ◽  
...  
Keyword(s):  
Extraction Method ◽  
Industrial Revolution ◽  
Near Field ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Feature Points ◽  
Lane Departure ◽  
Cloudy Weather ◽  
Forward Collision Warning

Recently, various technologies and sciences are developed for fourth industrial revolution which includes artificial intelligence, robotics, internet of things, 3-D printing, and autonomous transportation. The autonomous transportation is one of the fourth industrial technology, and has been studying for safety driving. Accurate lane detection and lane departure warning system is very important for autonomous transportation. However, conventional methods have some problems of applying real-driving such as extracting miss lanes under cloudy weather and vehicle disturbance situations. In order to solve the real driving problems, we propose a new lane detection technique for lane departure and forward collision warning system using a single in-vehicle camera. The proposed method consists of triangular lane model, feature points extraction method. In the near field, a triangular lane model is used to approximate a pair of lane boundaries. Subsequently, feature points extraction method based on hyperbola curve model is applied to obtain lane curvature in the far field. Mathematical B-spline applied to feature points for curved lane fitting. Simulation results show that the proposed lane detection and tracking method has good performance.

scholarly journals Lane Detection and Lane Departure Warning System

2021 ◽  
Vol 10 (3) ◽  
pp. 2212-2218
Keyword(s):  
Autonomous Vehicles ◽  
Warning System ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Road Accidents ◽  
Lane Departure ◽  
Clustering And Classification ◽  
Canny Edge ◽  
Boundary Information

Lane detection is important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. Advanced driverassistance systems are developed to assist drivers in the driving process reducing road accidents. In this work, we present an end-to-end system for lane identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. The first step is camera calibration which is used to remove the effect of lens distortion. Then a canny edge detection algorithm finds the edges of the images. Then the region of interest (ROI) is selected. The ROI is actually based on the rectangular shape appearing at the bottom of the image. ROI removes the unwanted region in the image. The potential lane markers are then determined using the Hough transform to analyze lane boundaries. Once the lane pixels are found, these pixels are continuously scanned to obtain the best linear regression analysis.It is qualified to be applied on highways and urban roadways. It also has been successfully verified in sunny, and rainy conditions for both day and night.

scholarly journals A Real-Time Lane Detection Algorithm Based on Intelligent CCD Parameters Regulation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ping-shu Ge ◽  
Lie Guo ◽  
Guo-kai Xu ◽  
Rong-hui Zhang ◽  
Tao Zhang
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Real ◽  
Lane Departure ◽  
Lane Line ◽  
Onboard System ◽  
Complexity Of Algorithm

Lane departure warning system (LDWS) has been regarded as an efficient method to lessen the damages of road traffic accident resulting from driver fatigue or inattention. Lane detection is one of the key techniques for LDWS. To overcome the contradiction between complexity of algorithm and the real-time requirement for vehicle onboard system, this paper introduces a new lane detection method based on intelligent CCD parameters regulation. In order to improve the real-time capability of the system, a CCD parameters regulating method is proposed which enhances the contrast between lane line and road surfaces and reduces image noise, so it lays a good foundation for the following lane detection. Hough transform algorithm is improved by selection and classification of seed points. Finally the lane line is extracted through some restrictions. Experimental results verify the effectiveness of the proposed method, which improves not only real-time capability but also the accuracy of the system.

A Novel Method for Tramcar Anti-Collision Warning System Based on Monocular Vision

2012 ◽  
Vol 430-432 ◽  
pp. 1871-1876
Author(s):  
Hui Bo Bi ◽  
Xiao Dong Xian ◽  
Li Juan Huang
Keyword(s):  
Software Design ◽  
Early Warning System ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
System Construction ◽  
Collision Warning ◽  
Lane Width ◽  
Novel Method

For the problem of tramcar collision accident in coal mine underground, a monocular vision-based tramcar anti-collision warning system based on ARM and FPGA was designed and implemented. In this paper, we present an improved fast lane detection algorithm based on Hough transform. Besides, a new distance measurement and early-warning system based on the invariance of the lane width is proposed. System construction, hardware architecture and software design are given in detail. The experiment results show that the precision and speed of the system can satisfy the application requirement.

A Novel Real-Time Lighting-Invariant Lane Departure Warning System

2018 ◽  
pp. 231-252
Author(s):  
Yassin Kortli ◽  
Mehrez Marzougui ◽  
Mohamed Atri
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Essential Element ◽  
Warning System ◽  
Lane Detection ◽  
Accuracy Evaluation ◽  
Lane Departure Warning ◽  
Lighting Conditions ◽  
Lane Departure ◽  
Driving Assistance

In recent years, in order to minimize traffic accidents, developing driving assistance systems for security has attracted much attention. Lane detection is an essential element of avoiding accidents and enhancing driving security. In this chapter, the authors implement a novel real-time lighting-invariant lane departure warning system. The proposed methodology works well in different lighting conditions, such as in poor conditions. The experimental results and accuracy evaluation indicates the efficiency of the system proposed for lane detection. The correct detection rate averages 97% and exceeds 95.6% in poor conditions. Furthermore, the entire process has only 29 ms per frame.

scholarly journals A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4028 ◽  
Author(s):  
Lu ◽  
Xu ◽  
Shan ◽  
Liu ◽  
Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Sample ◽  
Detection Method ◽  
Lane Detection ◽  
Feature Points ◽  
True Positive ◽  
Lane Departure ◽  
Different Types ◽  
Aerial View ◽  
Random Sample Consensus

Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes’ characteristics. Second, a ridge detector is proposed to extract each lane’s feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios.

Real-time illumination invariant lane detection for lane departure warning system

2015 ◽  
Vol 42 (4) ◽  
pp. 1816-1824 ◽  
Author(s):  
Jongin Son ◽  
Hunjae Yoo ◽  
Sanghoon Kim ◽  
Kwanghoon Sohn
Keyword(s):  
Real Time ◽  
Warning System ◽  
Lane Detection ◽  
Lane Departure Warning ◽  
Illumination Invariant ◽  
Lane Departure

scholarly journals Vision-Based Lane Line Detection System

2021 ◽  
Vol 09 (06) ◽  
Author(s):  
Poorna Vishwanth ◽  
Keyword(s):  
Automobile Industry ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Line Detection ◽  
Vertical Edge ◽  
Horizontal Edge ◽  
Driver Assistance

Since the 1990s, the rising key issue of the automobile industry is self-driving or driverless vehicles. Apparently, one of the most important challenges for smart self-driving cars comprises lane-detecting and lane-tracking capability to ensure safety and also decrease vehicle accidents for driver assistance systems. Since road lane detection is one of the most challenging tasks, driverless vehicles must learn to observe the road from a visual perspective in order to achieve automatic driving. Most of the research Works done so far can only detect the lanes or vehicles separately. However, in this paper, we propose a method to combine lane information and vehicle/obstacle information that can support the driver assistance system, driver warning system or the lane change assistant system so that it enhances the quality of results. For the lane changing system, the system detects or tracks the lane lines and detects the vehicles on all sides of a test vehicle. In lane detection, line detection algorithms such as the Canny Edge detection algorithm are used to detect the lane edges. Kalman filter will be used to track the vehicle detected from the vehicle detection algorithm. For vehicle detection, we use Otsu’s thresholding, horizontal edge filtering and vertical edge. The vertical edge filter and the Otsu’s thresholding are used to detect the vehicles on all sides of the test vehicles, then the horizontal edge is used to verify the vehicles detected.

scholarly journals Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

2021 ◽  
Vol 13 (16) ◽  
pp. 8759
Author(s):  
Wei Ye ◽  
Yueru Xu ◽  
Feixiang Zhou ◽  
Xiaomeng Shi ◽  
Zhirui Ye
Keyword(s):  
Mixed Model ◽  
Warning System ◽  
Gaussian Mixture ◽  
Bus Drivers ◽  
Collision Warning ◽  
Lane Departure ◽  
Driving Assistance ◽  
Naturalistic Driving ◽  
Forward Collision Warning ◽  
Driving Assistance System

Road crashes cause serious loss of life and property. Among all vehicles, buses are more likely to encounter crashes. In recent years, the advanced driving assistance system (ADAS) has been widely used in buses to improve safety. The warning system is one of the key functions and has proven effective in reducing crashes. However, drivers often ignore or overreact to ADAS warnings during naturalistic driving scenarios. Therefore, reactions of bus drivers to warnings need further investigation. In this study, bus drivers’ responses to lane departure warning (LDW) and forward collision warning (FCW) were investigated using 20-day naturalistic driving data. These reactions could be classified into three categories, namely positive, negative, and overreaction or emergency, by employing the Gaussian mixture model. The authors constructed a framework to quantify drivers’ reactions to the warning and study the reaction characteristics in different environments. The results indicate that drivers’ reactions to FCW were more positive than to LDW, drivers reacted more positively to LDW and FCW while driving on highways than on urban roads, and drivers reacted more positively at night to LDW and FCW than during daytime. This study gives support to an adaptive ADAS considering varying bus driver characteristics and environments.

scholarly journals UAV Collision Detection Algorithm based on Formulafor Fast Calculation

2021 ◽  
Vol 12 (6) ◽  
pp. 452-456
Author(s):  
Jung Kyu Park Et.al
Keyword(s):  
Collision Detection ◽  
Movement Time ◽  
Warning System ◽  
Detection Algorithm ◽  
Vehicle Safety ◽  
Collision System ◽  
Fast Calculation ◽  
Collision Warning ◽  
Collision Avoidance System ◽  
Forward Collision Warning

Collision Avoidance System (CAS) is known as a pre-collision system or a forward collision warning system, and research has first begun as a vehicle safety system. In this paper, we propose an algorithm for collision detection of UAV. The proposed algorithm uses a mathematical method and detects the collision of the UAV by modeling it in a two-dimensional plane. Using the mathematical modeling method, it is possible to determine the collision location of the UAV in advance. Experiments were conducted to measure the performance and accuracy of the proposed algorithm. In the experiment, we proceeded assuming three environments and were able to detect an accurate collision when the UAV moved. By applying the algorithm proposed in the paper to CAS, many collision accidents can be prevented. The proposed algorithm detects collisions through mathematical calculations. In addition, the movement time of the UAV was modeled in a 2D environment to shorten the calculation time.

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