scholarly journals Real-Time Lane Line Tracking Algorithm to Mini Vehicles

2021 ◽  
Vol 22 (4) ◽  
pp. 461-470
Author(s):  
Jozsef Suto
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Lane Detection ◽  
Tracking Algorithm ◽  
Single Camera ◽  
Autonomous Cars ◽  
Lane Line ◽  
Line Tracking

Abstract Autonomous navigation is important not only in autonomous cars but also in other transportation systems. In many applications, an autonomous vehicle has to follow the curvature of a real or artificial road or in other words lane lines. In those application, the key is the lane detection. In this paper, we present a real-time lane line tracking algorithm mainly designed to mini vehicles with relatively low computation capacity and single camera sensor. The proposed algorithm exploits computer vision techniques in combination with digital filtering. To demonstrate the performance of the method, experiments are conducted in an indoor, self-made test track where the effect of several external influencing factors can be observed. Experimental results show that the proposed algorithm works well independently of shadows, bends, reflection and lighting changes.

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.

scholarly journals A Real Time Lane Detection Algorithm Using LRF for Autonomous Navigation of a Mobile Robot

2013 ◽  
Vol 19 (11) ◽  
pp. 1029-1035 ◽  
Author(s):  
Hyun Woo Kim ◽  
Yo-Seup Hawng ◽  
Yun-Ki Kim ◽  
Dong-Hyuk Lee ◽  
Jang-Myung Lee
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Autonomous Navigation ◽  
Detection Algorithm ◽  
Lane Detection

Real-time lane detection and tracking for autonomous vehicle applications

2019 ◽  
Vol 233 (9) ◽  
pp. 2301-2311 ◽  
Author(s):  
Xinyu Jiao ◽  
Diange Yang ◽  
Kun Jiang ◽  
Chunlei Yu ◽  
Tuopu Wen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Detection Method ◽  
Autonomous Vehicle ◽  
Lane Detection ◽  
Vanishing Point ◽  
Lane Tracking ◽  
Detection And Tracking ◽  
Vanishing Point Detection ◽  
Point Detection

This article proposes an improved lane detection and tracking method for autonomous vehicle applications. In real applications, when the pose and position of the camera are changed, parameters and thresholds in the algorithms need fine adjustment. In order to improve adaptability to different perspective conditions, a width-adaptive lane detection method is proposed. As a useful reference to reduce noises, vanishing point is widely applied in lane detection studies. However, vanishing point detection based on original image consumes many calculation resources. In order to improve the calculation efficiency for real-time applications, we proposed a simplified vanishing point detection method. In the feature extraction step, a scan-line method is applied to detect lane ridge features, the width threshold of which is set automatically based on lane tracking. With clustering, validating, and model fitting, lane candidates are obtained from the basic ridge features. A lane-voted vanishing point is obtained by the simplified grid-based method, then applied to filter out noises. Finally, a multi-lane tracking Kalman filter is applied, the confirmed lines of which also provide adaptive width threshold for ridge feature extraction. Real-road experimental results based on our intelligent vehicle testbed proved the validity and robustness of the proposed method.

Region of Attraction for a Vehicle Pose Estimator Utilizing Monocular Vision and Lane Marker Maps

2016 ◽  
Author(s):  
Robert D. Leary ◽  
Sean Brennan
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
Detection Methods ◽  
Region Of Attraction ◽  
Vehicle Localization

Currently, there is a lack of low-cost, real-time solutions for accurate autonomous vehicle localization. The fusion of a precise a priori map and a forward-facing camera can provide an alternative low-cost method for achieving centimeter-level localization. This paper analyzes the position and orientation bounds, or region of attraction, with which a real-time vehicle pose estimator can localize using monocular vision and a lane marker map. A pose estimation algorithm minimizes the residual pixel-level error between the estimated and detected lane marker features via Gauss-Newton nonlinear least-squares. Simulations of typical road scenes were used as ground truth to ensure the pose estimator will converge to the true vehicle pose. A successful convergence was defined as a pose estimate that fell within 5 cm and 0.25 degrees of the true vehicle pose. The results show that the longitudinal vehicle state is weakly observable with the smallest region of attraction. Estimating the remaining five vehicle states gives repeatable convergence within the prescribed convergence bounds over a relatively large region of attraction, even for the simple lane detection methods used herein. A main contribution of this paper is to demonstrate a repeatable and verifiable method to assess and compare lane-based vehicle localization strategies.

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.

Real-time lane detection for autonomous navigation

2002 ◽  
Author(s):  
Seung Gweon Jeong ◽  
Chang Sup Kim ◽  
Kang Sup Yoon ◽  
Jong Nyun Lee ◽  
Jong Il Bae ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Lane Detection

scholarly journals Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering

2021 ◽  
Vol 5 (3) ◽  
pp. 206
Author(s):  
Chuho Yi ◽  
Jungwon Cho
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Depth Map ◽  
Time Estimation ◽  
Weather Conditions ◽  
Depth Estimation ◽  
Normal Vector ◽  
Single Camera ◽  
Distance Information ◽  
Lower Accuracy

Estimating a road surface or planes for applying AR(Augmented Reality) or an autonomous vehicle using a camera requires significant computation. Vision sensors have lower accuracy in distance measurement than other types of sensor, and have the difficulty that additional algorithms for estimating data must be included. However, using a camera has the advantage of being able to extract various information such as weather conditions, sign information, and road markings that are difficult to measure with other sensors. Various methods differing in sensor type and configuration have been applied. Many of the existing studies had generally researched by performing the depth estimation after the feature extraction. However, recent studies have suggested using deep learning to skip multiple processes and use a single DNN(Deep Neural Network). Also, a method using a limited single camera instead of a method using a plurality of sensors has been proposed. This paper presents a single-camera method that performs quickly and efficiently by employing a DNN to extract distance information using a single camera, and proposes a modified method for using a depth map to obtain real-time surface characteristics. First, a DNN is used to estimate the depth map, and then for quick operation, normal vector that can connect similar planes to depth is calculated, and a clustering method that can be connected is provided. An experiment is used to show the validity of our method, and to evaluate the calculation time.

scholarly journals Real-Time Lane Detection and Object Recognition in Self-Driving Car using YOLO neural network and Computer Vision

2020 ◽  
Vol 9 (7S) ◽  
pp. 87-92
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Vital Role ◽  
Lane Detection ◽  
Movement Planning ◽  
The Road ◽  
Localization And Mapping ◽  
Prediction Probability ◽  
Canny Edge

The Darwinism of Artificial Intelligence and robotics has grown up incredibly. Recently, there are a lot of progress have been undertaken in the context of Autonomous vehicle. Robo-car or self driving car consist many module like localization and mapping, scene understanding, movement planning, and driver state. In movement planning lane perception and recognition of the object plays vital role. This proposed state-of-art recognizes the road track in the video‘s frame and perform lane detection using canny edge detector and Hough transform algorithm. In this paper, Object recognition is possible with help of YOLO (you only look once) which is one of the real time CNN methods aims to detect object inside the image as part of road track. The result manifests the road lane detection guidance and object recognition along with prediction probability and bounding box

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