Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanceOver the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanc

Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

Unintentional lane departure accidents are one of the biggest reasons for the causalities that occur due to human errors. By incorporating lane-keeping features in vehicles, many accidents can be avoided. The lane-keeping system operates by auto-steering the vehicle in order to keep it within the desired lane, despite of changes in road conditions and other interferences. Accurate steering angle prediction is crucial to keep the vehicle within the road boundaries, which is a challenging task. The main difficulty in this regard is to identify the drivable road area on heterogeneous road types varying in color, texture, illumination conditions, and lane marking types. This strenuous problem can be addressed by two approaches, namely, 'computer-vision-based approach' and 'imitation-learning-based approach'. To the best of our knowledge, at present, there is no such detailed review study covering both the approaches and their related optimization techniques. This comprehensive review attempts to provide a clear picture of both approaches of steering angle prediction in the form of step by step procedures. The taxonomy of steering angle prediction has been presented in the paper for a better comprehension of the problem. We have also discussed open research problems at the end of the paper to help the researchers of this area to discover new research horizons

무인 자율주행 차량을 위한 단안 카메라 기반 실시간 주행 환경 인식 기법에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기공학부, 2014. 2. 서승우.Homo Faber, refers to humans as controlling the environments through tools. From the beginning of the world, humans create tools for chasing the convenient life. The desire for the rapid movement let the human ride on horseback, make the wagon and finally make the vehicle. The vehicle made humans possible to travel the long distance very quickly as well as conveniently. However, since human being itself is imperfect, plenty of people have died due to the car accident, and people are dying at this moment. The research for autonomous vehicle has been conducted to satisfy the humans desire of the safety as the best alternative. And, the dream of autonomous vehicle will be come true in the near future. For the implementation of autonomous vehicle, many kinds of techniques are required, among which, the recognition of the environment around the vehicle is one of the most fundamental and important problems. For the recognition of surrounding objects many kinds of sensors can be utilized, however, the monocular camera can collect the largest information among sensors as well as can be utilized for the variety of purposes, and can be adopted for the various vehicle types due to the good price competitiveness. I expect that the research using the monocular camera for autonomous vehicle is very practical and useful. In this dissertation, I cover four important recognition problems for autonomous driving by using monocular camera in vehicular environment. Firstly, to drive the way autonomously the vehicle has to recognize lanes and keep its lane. However, the detection of lane markings under the various illuminant variation is very difficult in the image processing area. Nevertheless, it must be solved for the autonomous driving. The first research topic is the robust lane marking extraction under the illumination variations for multilane detection. I proposed the new lane marking extraction filter that can detect the imperfect lane markings as well as the new false positive cancelling algorithm that can eliminate noise markings. This approach can extract lane markings successfully even under the bad illumination conditions. Secondly, the problem to tackle, is if there is no lane marking on the road, then how the autonomous vehicle can recognize the road to run? In addition, what is the current lane position of the road? The latter is the important question since we can make a decision for lane change or keeping depending on the current position of lane. The second research is for handling those two problems, and I proposed the approach for the fusing the road detection and the lane position estimation. Thirdly, to drive more safely, keeping the safety distance is very important. Additionally, many equipments for the driving safety require the distance information. Measuring accurate inter-vehicle distance by using monocular camera and line laser is the third research topic. To measure the inter-vehicle distance, I illuminate the line laser on the front side of vehicle, and measure the length of the laser line and lane width in the image. Based on the imaging geometry, the distance calculation problem can be solved with accuracy. There are still many of important problems are remaining to be solved, and I proposed some approaches by using the monocular camera to handle the important problems. I expect very active researches will be continuously conducted and, based on the researches, the era of autonomous vehicle will come in the near future.1 Introduction 1.1 Background and Motivations 1.2 Contributions and Outline of the Dissertation 1.2.1 Illumination-Tolerant Lane Marking Extraction for Multilane Detection 1.2.2 Fusing Road Detection and Lane Position Estimation for the Robust Road Boundary Estimation 1.2.3 Accurate Inter-Vehicle Distance Measurement based on Monocular Camera and Line Laser 2 Illumination-Tolerant Lane Marking Extraction for Multilane Detection 2.1 Introduction 2.2 Lane Marking Candidate Extraction Filter 2.2.1 Requirements of the Filter 2.2.2 A Comparison of Filter Characteristics 2.2.3 Cone Hat Filter 2.3 Overview of the Proposed Algorithm 2.3.1 Filter Width Estimation 2.3.2 Top Hat (Cone Hat) Filtering 2.3.3 Reiterated Extraction 2.3.4 False Positive Cancelling 2.3.4.1 Lane Marking Center Point Extraction 2.3.4.2 Fast Center Point Segmentation 2.3.4.3 Vanishing Point Detection 2.3.4.4 Segment Extraction 2.3.4.5 False Positive Filtering 2.4 Experiments and Evaluation 2.4.1 Experimental Set-up 2.4.2 Conventional Algorithm for Evaluation 2.4.2.1 Global threshold 2.4.2.2 Positive Negative Gradient 2.4.2.3 Local Threshold 2.4.2.4 Symmetry Local Threshold 2.4.2.5 Double Extraction using Symmetry Local Threshold 2.4.2.6 Gaussian Filter 2.4.3 Experimental Results 2.4.4 Summary 3 Fusing Road Detection and Lane Position Estimation for the Robust Road Boundary Estimation 3.1 Introduction 3.2 Chromaticity-based Flood-fill Method 3.2.1 Illuminant-Invariant Space 3.2.2 Road Pixel Selection 3.2.3 Flood-fill Algorithm 3.3 Lane Position Estimation 3.3.1 Lane Marking Extraction 3.3.2 Proposed Lane Position Detection Algorithm 3.3.3 Birds-eye View Transformation by using the Proposed Dynamic Homography Matrix Generation 3.3.4 Next Lane Position Estimation based on the Cross-ratio 3.3.5 Forward-looking View Transformation 3.4 Information Fusion Between Road Detection and Lane Position Estimation 3.4.1 The Case of Detection Failures 3.4.2 The Benefit of Information Fusion 3.5 Experiments and Evaluation 3.6 Summary 4 Accurate Inter-Vehicle Distance Measurement based on Monocular Camera and Line Laser 4.1 Introduction 4.2 Proposed Distance Measurement Algorithm 4.3 Experiments and Evaluation 4.3.1 Experimental System Set-up 4.3.2 Experimental Results 4.4 Summary 5 ConclusionDocto

Road Tunnel Entrance Recognition System

Denne oppgaven beskriver et system for gjenkjenning av tunnelåpninger som er ment for installasjon i bil. Systemet vil hjelpe sjåføren til å fokusere på veien og andre trafikanter når han kjører gjennom en tunnel. Når systemet gjenkjenner en tunnelåpning vil det starte andre programmer som sikrer at frontlys og instrumentbelysningen er på. System vil også kunne fjerne eventuell dugg som legger seg på frontruten når man kommer inn i en kald tunnel. I tillegg vil ventilasjonssystemet slås over på resirkulasjon slik at ikke forurenset luft fra tunnelen trekkes inn i bilen. Ved å gjøre dette automatisk, vil kjøreturen gjennom tunnelen gjøres sikrere

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification. Document type: Articl