Modified Hough Transform for Road Lane Colorization to Prevent Accidents

Lane coloration is becoming popular in real time vehicular ad-hoc network. This research work focus on providing better performance in lane coloration algorithm by using CLAHE to enhance the input image and also by modifying the Hough transform using the dynamic thresholding to detect curve lanes. Main emphasis is to improve the result of lane coloration algorithm when fog, noise or any other factor is present in the images. The methods developed so far are working efficiently and giving good results in case when the straight lane road images are there. But problem is that they fail or not give efficient results when there are curved lane road images. The experiments results for the road images have shown the significant improvement of the proposed technique over the available one. DOI: 10.17762/ijritcc2321-8169.15010

Sensor fusion methodology for vehicle detection

A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriage-ways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles - Technological and Methodical Approaches

Fahrerassistenzsysteme sowie automatisiertes Fahren leisten einen wesentlichen Beitrag zur Verbesserung der Verkehrssicherheit von Kraftfahrzeugen, insbesondere von Nutzfahrzeugen. Mit der Weiterentwicklung des automatisierten Fahrens steigt hierbei die funktionale Leistungsfähigkeit, woraus Anforderungen an neue, gesamtheitliche Erprobungskonzepte entstehen. Um die Absicherung höherer Stufen von automatisierten Fahrfunktionen zu garantieren, sind neuartige Verifikations- und Validierungsmethoden erforderlich. Ziel dieser Arbeit ist es, durch die Aggregation von Testergebnissen aus wissensbasierten und datengetriebenen Testplattformen den Übergang von einer quantitativen Kilometerzahl zu einer qualitativen Testabdeckung zu ermöglichen. Die adaptive Testabdeckung zielt somit auf einen Kompromiss zwischen Effizienz- und Effektivitätskriterien für die Absicherung von automatisierten Fahrfunktionen in der Produktentstehung von Nutzfahrzeugen ab. Diese Arbeit umfasst die Konzeption und Implementierung eines modularen Frameworks zur kundenorientierten Absicherung automatisierter Fahrfunktionen mit vertretbarem Aufwand. Ausgehend vom Konfliktmanagement für die Anforderungen der Teststrategie werden hochautomatisierte Testansätze entwickelt. Dementsprechend wird jeder Testansatz mit seinen jeweiligen Testzielen integriert, um die Basis eines kontextgesteuerten Testkonzepts zu realisieren. Die wesentlichen Beiträge dieser Arbeit befassen sich mit vier Schwerpunkten: * Zunächst wird ein Co-Simulationsansatz präsentiert, mit dem sich die Sensoreingänge in einem Hardware-in-the-Loop-Prüfstand mithilfe synthetischer Fahrszenarien simulieren und/ oder stimulieren lassen. Der vorgestellte Aufbau bietet einen phänomenologischen Modellierungsansatz, um einen Kompromiss zwischen der Modellgranularität und dem Rechenaufwand der Echtzeitsimulation zu erreichen. Diese Methode wird für eine modulare Integration von Simulationskomponenten, wie Verkehrssimulation und Fahrdynamik, verwendet, um relevante Phänomene in kritischen Fahrszenarien zu modellieren. * Danach wird ein Messtechnik- und Datenanalysekonzept für die weltweite Absicherung von automatisierten Fahrfunktionen vorgestellt, welches eine Skalierbarkeit zur Aufzeichnung von Fahrzeugsensor- und/ oder Umfeldsensordaten von spezifischen Fahrereignissen einerseits und permanenten Daten zur statistischen Absicherung und Softwareentwicklung andererseits erlaubt. Messdaten aus länderspezifischen Feldversuchen werden aufgezeichnet und zentral in einer Cloud-Datenbank gespeichert. * Anschließend wird ein ontologiebasierter Ansatz zur Integration einer komplementären Wissensquelle aus Feldbeobachtungen in ein Wissensmanagementsystem beschrieben. Die Gruppierung von Aufzeichnungen wird mittels einer ereignisbasierten Zeitreihenanalyse mit hierarchischer Clusterbildung und normalisierter Kreuzkorrelation realisiert. Aus dem extrahierten Cluster und seinem Parameterraum lassen sich die Eintrittswahrscheinlichkeit jedes logischen Szenarios und die Wahrscheinlichkeitsverteilungen der zugehörigen Parameter ableiten. Durch die Korrelationsanalyse von synthetischen und naturalistischen Fahrszenarien wird die anforderungsbasierte Testabdeckung adaptiv und systematisch durch ausführbare Szenario-Spezifikationen erweitert. * Schließlich wird eine prospektive Risikobewertung als invertiertes Konfidenzniveau der messbaren Sicherheit mithilfe von Sensitivitäts- und Zuverlässigkeitsanalysen durchgeführt. Der Versagensbereich kann im Parameterraum identifiziert werden, um die Versagenswahrscheinlichkeit für jedes extrahierte logische Szenario durch verschiedene Stichprobenverfahren, wie beispielsweise die Monte-Carlo-Simulation und Adaptive-Importance-Sampling, vorherzusagen. Dabei führt die geschätzte Wahrscheinlichkeit einer Sicherheitsverletzung für jedes gruppierte logische Szenario zu einer messbaren Sicherheitsvorhersage. Das vorgestellte Framework erlaubt es, die Lücke zwischen wissensbasierten und datengetriebenen Testplattformen zu schließen, um die Wissensbasis für die Abdeckung der Operational Design Domains konsequent zu erweitern. Zusammenfassend zeigen die Ergebnisse den Nutzen und die Herausforderungen des entwickelten Frameworks für messbare Sicherheit durch ein Vertrauensmaß der Risikobewertung. Dies ermöglicht eine kosteneffiziente Erweiterung der Validität der Testdomäne im gesamten Softwareentwicklungsprozess, um die erforderlichen Testabbruchkriterien zu erreichen

무인자율주행을 위한 도로 지도 생성 및 측위

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 서승우.This dissertation aims to present precise and cost-efficient mapping and localization algorithms for autonomous vehicles. Mapping and localization are ones of the key components in autonomous vehicles. The major concern for mapping and localization research is maximizing the accuracy and precision of the systems while minimizing the cost. For this goal, this dissertation proposes a road map generation system to create a precise and efficient lane-level road map, and a localization system based on the proposed road map and affordable sensors. In chapter 2, the road map generation system is presented. The road map generation system integrates a 3D LIDAR data and high-precision vehicle positioning system to acquire accurate road geometry data. Acquired road geometry data is represented as sets of piecewise polynomial curves in order to increase the storage efficiency and the usability. From extensive experiments using a real urban and highway road data, it is verified that the proposed road map generation system generates a road map that is accurate and more efficient than previous road maps in terms of the storage efficiency and usability. In chapter 3, the localization system is presented. The localization system targets an environment that the localization is difficult due to the lack of feature information for localization. The proposed system integrates the lane-level road map presented in chapter 2, and various low-cost sensors for accurate and cost-effective vehicle localization. A measurement ambiguity problem due to the use of low-cost sensors and poor feature information was presented, and a probabilistic measurement association-based particle filter is proposed to resolve the measurement ambiguity problem. Experimental results using a real highway road data is presented to verify the accuracy and reliability of the localization system. In chapter 4, an application of the accurate vehicle localization system is presented. It is demonstrated that sharing of accurate position information among vehicles can improve the traffic flow and suppress the traffic jam effectively. The effect of the position information sharing is evaluated based on numerical experiments. For this, a traffic model is proposed by extending conventional SOV traffic model. The numerical experiments show that the traffic flow is increased based on accurate vehicle localization and information sharing among vehicles.Chapter 1 Introduction 1 1.1 Background andMotivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Generation of a Precise and Efficient Lane-Level Road Map 3 1.2.2 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 4 1.2.3 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 4 Chapter 2 Generation of a Precise and Efficient Lane-Level Road Map 6 2.1 RelatedWorks 9 2.1.1 Acquisition of Road Geometry 11 2.1.2 Modeling of Road Geometry 13 2.2 Overall System Architecture 15 2.3 Road Geometry Data Acquisition and Processing 17 2.3.1 Data Acquisition 18 2.3.2 Data Processing 18 2.3.3 Outlier Problem 26 2.4 RoadModeling 27 2.4.1 Overview of the sequential approximation algorithm 29 2.4.2 Approximation Process 30 2.4.3 Curve Transition 35 2.4.4 Arc length parameterization 38 2.5 Experimental Validation 39 2.5.1 Experimental Setup 39 2.5.2 Data Acquisition and Processing 40 2.5.3 RoadModeling 42 2.6 Summary 49 Chapter 3 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 51 3.1 RelatedWorks 53 3.2 SystemOverview 57 3.2.1 Test Vehicle and Sensor Configuration 57 3.2.2 Augmented RoadMap Data 57 3.2.3 Vehicle Localization SystemArchitecture 61 3.2.4 ProblemStatement 62 3.3 Particle filter-based Vehicle Localization Algorithm 63 3.3.1 Initialization 65 3.3.2 Time Update 66 3.3.3 Measurement Update 66 3.3.4 Integration 68 3.3.5 State Estimation 68 3.3.6 Resampling 69 3.4 Map-Image Measurement Update with Probabilistic Data Association 69 3.4.1 Lane Marking Extraction and Measurement Error Model 70 3.5 Experimental Validation 76 3.5.1 Experimental Environments 76 3.5.2 Localization Accuracy 77 3.5.3 Effect of the Probabilistic Measurement Association 79 3.5.4 Effect of theMeasurement ErrorModel 80 3.6 Summary 80 Chapter 4 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 82 4.1 Extended SOVModel 84 4.1.1 SOVModel 85 4.1.2 Extended SOVModel 89 4.2 Results and Discussions 91 4.3 Summary 93 Chapter 5 Conclusion 95 Bibliography 97 국문 초록 108Docto

Topics in Machining with Industrial Robot Manipulators and Optimal Motion Control

Two main topics are considered in this thesis: Machining with industrial robot manipulators and optimal motion control of robots and vehicles. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of the strong process forces required for the task. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, may significantly degrade the achieved machining accuracy of the manufactured part. In this thesis, a macro/micro-manipulator configuration is considered to the purpose of increasing the milling accuracy. In particular, a model-based control architecture is developed for control of the macro/micro-manipulator setup. The considered approach is validated by experimental results from extensive milling experiments in aluminium and steel. Related to the problem of high-accuracy milling is the topic of robot modeling. To this purpose, two different approaches are considered; modeling of the quasi-static joint dynamics and dynamic compliance modeling. The first problem is approached by an identification method for determining the joint stiffness and backlash. The second problem is approached by using gray-box identification based on subspace-identification methods. Both identification algorithms are evaluated experimentally. Finally, online state estimation is considered as a means to determine the workspace position and orientation of the robot tool. Kalman Filters and Rao-Blackwellized Particle Filters are employed to the purpose of sensor fusion of internal robot measurements and measurements from an inertial measurement unit for estimation of the desired states. The approaches considered are fully implemented and evaluated on experimental data. The second part of the thesis discusses optimal motion control applied to robot manipulators and road vehicles. A control architecture for online control of a robot manipulator in high-performance path tracking is developed, and the architecture is evaluated in extensive simulations. The main characteristic of the control strategy is that it combines coordinated feedback control along both the tangential and transversal directions of the path; this separation is achieved in the framework of natural coordinates. One motivation for research on optimal control of road vehicles in time-critical maneuvers is the desire to develop improved vehicle-safety systems. In this thesis, a method for solving optimal maneuvering problems using nonlinear optimization is discussed. More specifically, vehicle and tire modeling and the optimization formulations required to get useful solutions to these problems are investigated. The considered method is evaluated on different combinations of chassis and tire models, in maneuvers under different road conditions, and for investigation of optimal maneuvers in systems for electronic stability control. The obtained optimization results in simulations are evaluated and compared

Moving object detection for automobiles by the shared use of H.264/AVC motion vectors : innovation report.

Cost is one of the problems for wider adoption of Advanced Driver Assistance Systems (ADAS) in China. The objective of this research project is to develop a low-cost ADAS by the shared use of motion vectors (MVs) from a H.264/AVC video encoder that was originally designed for video recording only. There were few studies on the use of MVs from video encoders on a moving platform for moving object detection. The main contribution of this research is the novel algorithm proposed to address the problems of moving object detection when MVs from a H.264/AVC encoder are used. It is suitable for mass-produced in-vehicle devices as it combines with MV based moving object detection in order to reduce the cost and complexity of the system, and provides the recording function by default without extra cost. The estimated cost of the proposed system is 50% lower than that making use of the optical flow approach. To reduce the area of region of interest and to account for the real-time computation requirement, a new block based region growth algorithm is used for the road region detection. To account for the small amplitude and limited precision of H.264/AVC MVs on relatively slow moving objects, the detection task separates the region of interest into relatively fast and relatively slow speed regions by examining the amplitude of MVs, the position of focus of expansion and the result of road region detection. Relatively slow moving objects are detected and tracked by the use of generic horizontal and vertical contours of rear-view vehicles. This method has addressed the problem of H.264/AVC encoders that possess limited precision and erroneous motion vectors for relatively slow moving objects and regions near the focus of expansion. Relatively fast moving objects are detected by a two-stage approach. It includes a Hypothesis Generation (HG) and a Hypothesis Verification (HV) stage. This approach addresses the problem that the H.264/AVC MVs are generated for coding efficiency rather than for minimising motion error of objects. The HG stage will report a potential moving object based on clustering the planar parallax residuals satisfying the constraints set out in the algorithm. The HV will verify the existence of the moving object based on the temporal consistency of its displacement in successive frames. The test results show that the vehicle detection rate higher than 90% which is on a par to methods proposed by other authors, and the computation cost is low enough to achieve the real-time performance requirement. An invention patent, one international journal paper and two international conference papers have been either published or accepted, showing the originality of the work in this project. One international journal paper is also under preparation

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria