Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed to make use of this information. In experiments with arti cial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance. 1.

One goal of research in artificial intelligence is to automate tasks that currently require human expertise; this automation is important because it saves time and brings problems that were previously too large to be solved into the feasible domain. Data analysis, or the ability to identify meaningful patterns and trends in large volumes of data, is an important task that falls into this category. Clustering algorithms are a particularly useful group of data analysis tools. These methods are used, for example, to analyze satellite images of the Earth to identify and categorize different land and foliage types or to analyze telescopic observations to determine what distinct types of astronomical bodies exist and to categorize each observation. However, most existing clustering methods apply general similarity techniques rather than making use of problem-specific information. This dissertation first presents a novel method for converting existing clustering algorithms into constrained clustering algorithms. The resulting methods are able to accept domain-specific information in the form of constraints on the output clusters. At the most general level, each constraint is an instance-level statement

This paper describes research that investigates the application of outdoor Augmented Reality (AR) for three-dimensional graphical simulation of construction activities. The objective of the research is an AR-based platform that can be used with corresponding peripheral equipment (Head Mounted Display, GPS receiver, orientation tracker, and a portable computer) to generate a mixed view of the real world and superimposed virtual construction graphics (CAD models) in an outdoor environment. What distinguishes the presented work from indoor AR applications is the capability to produce real time output as the user moves around by applying minimum constraints over the user’s position and orientation. In addition, the ability to operate independently of environmental factors such as lighting conditions makes the designed platform a powerful tool for outdoor AR applications. This paper presents the first results of our research and a prototype software called UM-AR-GPS-ROVER that is capable of interactively placing 3D CAD models at any desired location in outdoor augmented space. The concept and the prototype are demonstrated with an example in which scheduled construction activities for the erection of a structural steel frame are animated with the passage of simulated project time in outdoor AR.

In this paper we describe some of the research issues and challenges in image-based location awareness and navigation. We will describe two systems being developed at Purdue University as testbeds for our ideas. The main system architecture combines image processing, mobility, wireless communication, and location awareness. We will describe two fundamental scenarios for using images to aid in mobile navigation problems. The first provides the ability to use a locally acquired image to determine the identity of an object, for example a building, as one roams in an area. The second problem is the use of images in a database to aid in vehicle navigation. The solution to these problems use location information, such as GPS signals, to compare and search location-annotated images in a database. We believe location information can improve the accuracy in image database search. 1.

Many advanced safety and navigation applications in vehicles require accurate, detailed digital maps, but manual lane measurements are expensive and time-consuming, making automated techniques desirable. This paper describes a system that refines and augments commercial digital maps using position traces that come from Global Positioning System receivers with differential corrections. The computed lane models enable safety applications, such as lanekeeping, and convenience applications, such as lane-changing advice. We also present a number of evaluation metrics for estimating the quality of the enhanced maps we generate. Multiple position traces are a powerful new source of data that enables cheap, automated methods of inducing lane models, as well as other geographic knowledge, like traffic signals and elevations, and potentially impacts any geographic information system with a need to relate to actual behavior. Keywords Sensors (vehicle-based), Communications (vehicle-to-roadside),...

As mobile systems (such as laptops and mobile telephones) continue growing, navigation assistance and location-based services are becoming increasingly important. Existing technology allow mobile users to access Internet services (e.g. email and web surfing), simple multimedia services (e.g. music and video clips), and make telephone calls. However, the potential of advanced multimedia services has not been fully developed, especially multimedia for navigation or location based services. At Purdue University, we are developing an image database, known as LAID, in which every image is annotated with its location, compass heading, acquisition time, and weather conditions. LAID can be used to study several types of navigation problems: A mobile user can take an image and transmit the image to the LAID sever. The server compares the image with the images stored in the database to determine where the user is located. We refer to this as the “forward ” navigation problem. The second type of problem is to provide a “virtual tour on demand. ” A user inputs a starting and an ending addresses and LAID retrieves the images along a route that connects the two addresses. This is a generalization of route planning. Our database currently contains over 20000 images and covers approximately 25 % of the city of West Lafayette, Indiana.

Instead of traditional ways of creating road maps, an attractive alternative is to create a map based on GPS traces of regular drivers. One important aspect of this approach is to automatically compute the number and locations of driving lanes on a road. We introduce the idea of using a Gaussian mixture model (GMM) to model the distribution of GPS traces across multiple traffic lanes. The GMM naturally accounts for the inherent spread in GPS data. We present a new variation of the GMM that enforces constant lane width and GPS variance in each lane. For fitting the GMM, we also introduce a new regularizer that is sensitive to the overall spread of the GPS data across the road. Our experiments on real GPS data show that our new GMM is better at counting lanes than a more traditional GMM, and it gives more consistent results across our data set.

Abstract. As an alternative to expensive road surveys, we are working toward a method to infer the road network from GPS data logged from regular vehicles. One of the most important components of this problem is to find road intersections. We introduce an intersection detector that uses a localized shape descriptor to represent the distribution of GPS traces around a point. A classifier is trained on the shape descriptor to discriminate intersections from non-intersections, and we demonstrate its effectiveness with an ROC curve. In a second step, we use the GPS data to prune the detected intersections and connect them with geometrically accurate road segments. In the final step, we use the iterative closest point algorithm to more accurately localize the position of each intersection. We train and test our method on GPS data gathered from regular vehicles in the Seattle, WA, USA area. The tests show we can correctly find road intersections.

Abstract. Data mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent time, data mining studies have been carried out in many engineering disciplines. In this paper the background of data mining and tools is introduced. �urther applications of data mining to transportation engineering problems are reviewed. The application of data mining for typical example of ‘Vehicle Crash Study ’ is demonstrated using commercially available data mining tool. The paper highlights the potential of data mining tool application in transportation engineering sector.

Abstract. This paper describes research that investigated the application of the Global Positioning System (GPS) and 3DOF angular tracking to address the registration problem in visualization of construction graphics in outdoor Augmented Reality (AR) environments. AR is the overlaying of virtual images and computer-generated information over scenes of the real world so that the user’s resulting view is enhanced or augmented beyond the normal experience. One of the basic issues in AR is the registration problem. Objects in the real world and superimposed virtual objects must be properly aligned with respect to each other, or the illusion that the two coexist in augmented space is compromised. In the presented research, the global position and the 3D orientation of the user’s viewpoint (i.e. longitude, latitude, altitude, heading, pitch, and roll) are tracked, and this information is reconciled with the known global position and orientation of superimposed CAD objects. The result is an augmented outdoor environment where superimposed CAD objects stay fixed to their real world locations as the user moves freely on a construction site. The algorithms are implemented in a prototype platform called UM-AR-GPS-ROVER that is capable of interactively placing 3D CAD models at any desired location in an outdoor augmented space. 1