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

Does Familiarity breed inattention? Why drivers crash on the roads they know best

This paper describes our research into the nature of everyday driving, with a particular emphasis on the processes that govern driver behaviour in familiar, well - practiced situations. The research examined the development and maintenance of proceduralised driving habits in a high-fidelity driving simulator by paying 29 participants to drive a simulated road regularly over three months of testing. A range of measures, including detection task performance and driving performance were collected over the course of 20 sessions. Performance from a yoked control group who experienced the same road scenarios in a single session was also measured. The data showed the development of stereotyped driving patterns and changes in what drivers noticed, indicative of in attentional blindness and “driving without awareness”. Extended practice also resulted in increased sensitivity for detecting changes to foveal road features associated with vehicle guidance and performance on an embedded vehicle detection task (detection of a specific vehicle type). The changes in attentional focus and driving performance resulting from extended practice help explain why drivers are at increased risk of crashing on roads they know well. Identifying the features of familiar roads that attract driver attention, even when they are driving without awareness, can inform new interventions and designs for safer roads. The data also provide new light on a range of previous driver behaviour research including a “Tandem Model” that includes both explicit and implicit processes involved in driving performance