Abstract

We have developed a new traffic measuring tool and applied it to the real-time simulation of a network. It monitors IP traffic on an ATM link and continuously transfers the length and timestamp of each IP packet to a post-processing system. The post-processing system receives the data, estimates the cell's arrival epoch at the transmission queue of the ATM link, and simulates the queueing behavior on-line if conditions differ from those of the actual system. The measuring tool and real-time simulation rep resent a new approach to traffic engineering. A new estimation problem, the arrival sequence estimation, is shown and some algorithms are proposed and evaluated. Document type: Part of book or chapter of book

Abstract

Discussions of autonomous land vehicles often invoke the example of air traffic, where the autopilot is responsible for steering except for take-off and landing. The question arises: what can we learn from air traffic? What autonomously flying aircraft and autonomously driving vehicles have in common is that the pilot or driver bears the final responsibility. But, there are a number of differences between road traffic and air traffic (besides their type of locomotion) that make transferring the systems from one to the other impractical. Document type: Part of book or chapter of book

Abstract

Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006 This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Publicado Document type: Part of book or chapter of book

Abstract

Hydraulic steel structures, especially lock gates play a significant role in keeping navigation traffic uninterrupted. After a few decades of operation, many of the welded joints may suffer various degrees of deterioration, primarily due to fatigue. To economically combining crack inspection with a scheduled maintenance of the movable parts of the gate, it is valuable to predict inspection time of the welded joints using the historical operations of the gate, i.e. the variation of water levels. Updating failure probability of welded joint is mature in the offshore industry, but it is rarely applied for inland navigation lock gates where the contribution to fatigue failure comes from the variation of water pressures during operation of the lock gates. The scope of this paper is to predict the inspection time of a welded joint using the observed water levels from the operational history. The updating of the failure probability is done for three inspection techniques, considering annual probability and repair decisions. The results show the effects of critical annual probability and the probability of detections (PODs) on the update failure probability for a welded joint. Document type: Part of book or chapter of book

Abstract

Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle’s departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm. Document type: Part of book or chapter of book

Abstract

As future personal vehicles start enjoying the ability to fly, tackling safe transportation coordination can be a tremendous task, far beyond the current challenge on radar screen monitoring of the already saturated air traffic control. Our focus is on the distributed safe-distance coordination among a group of autonomous flying vehicle agents, where each follows its own current straight-line direction in a 3D space with variable speeds. A virtual spring-based model is proposed for the group coordination. Within a specified neighborhood radius, each vehicle forms a virtual connection with each neighbor vehicle by a virtual spring. As the vehicle changes its position, speed and altitude, the total resultant forces on each virtual spring try to maintain zero by moving to the mechanical equilibrium point. The agents then add the simple total virtual spring constraints to their movements to determine their next positions individually. Together, the multi-agent vehicles reach a group behavior, where each of them keeps a minimal safe-distance with others. A new safe behavior thus arises in the group level. With the proposed virtual spring coordination model, the vehicles need no direct communication with each other, require only minimum local processing resources, and the control is completely distributed. New behaviors can now be formulated and studied based on the proposed model, e.g., how a fast driving vehicle can find its way though the crowd by avoiding the other vehicles effortlessly1. Document type: Part of book or chapter of book

Abstract

In Multi-Layer networks, where more than one layer is dynamic, i.e., connections are set up using not only the upper, e.g., IP layer but the underlying wavelength layer as well leads often to suboptimal performance due to long wavelength paths, that do not allow routing the traffic along the shortest path. The role of MLTE (Multi-Layer Traffic Engineering) is to cut these long wavelength paths into parts (fragments) that allow better routing at the upper layer (fragmentation), or to concatenate two or more fragments into longer paths (defragmentation) when the network load is low and therefore less hops are preferred.\r \r In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and De-Fragmentation of wavelength paths making the network always instantly adapt to changing traffic conditions. We introduce the notion of shadow capacities to model “lightpath tailoring”. We implicitly assume that the wavelength paths carry such, e.g., IP traffic that can be interrupted for a few microseconds and that even allows minor packet reordering.\r \r To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study. Document type: Part of book or chapter of book

Abstract

The article considers the active safety system of road freight transport. The stereoscopic computer vision is the core of the system. The article also describes the major algorithms of active safety and the accuracy characteristics of algorithms’ application. Document type: Part of book or chapter of book

Abstract

This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences. Document type: Part of book or chapter of book

Abstract

In order to meet a tremendous amount of data storage requirement in next-generation wireless networks, an increasing number of cloud data centers has been deployed around the world. The underlying core networks are expected to provide the ability to store data in a dynamic and scalable computing environment. The traditional Internet Protocol (IP) has shown to be restricted due to its static architecture, which accordingly motivates the development of Software-Defined Networks (SDNs). In the SDNs, Traffic Engineering (TE) is simpler and programmable with a controller without the requirement of reconfiguration for all network devices. However, the existing TE algorithm of the SDNs rejects a number of requested flows caused by their undetermined routing paths where only flow bandwidth is considered in path determination. This paper proposes a Quality-of-Service (QoS) based Flow Assignment algorithm which enables the computation of end-to-end path for traffic flows guaranteeing the QoS requirements including bandwidth, end-to-end delay and packet loss probability. Through the Open Source Hybrid IP/SDNs platform, the proposed algorithm is validated and shown to significantly reduce flow rejection rate of up to 50% compared to the conventional approach, and therefore can be used to implement an effective DiffServ mechanism for flow allocation in the SDNs. Document type: Part of book or chapter of book