___________________________________________________________________Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for i) constructing trajectories from movement tracks, ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the paper surveys the new privacy issues that rise due to the semantic aspects of trajectories.

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user's perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.

Autonomous vehicles have great promise for applications in the military, a g r iculture, space exploration, and other domains. Moreover, rapid progress in miniaturization and improved cost-eectiveness of navigation sensors, cameras, and computers is accelerating the maturation of robotic vehicles. However, a key limitation remains for domains in which robots must navigate in tall grass, small bushes, or forested areas, because existing perception systems cannot do eective obstacle detection in these situations. Most obstacle detection systems to date rely exclusively on range data from ladar, stereo vision, radar, or ultrasonic sensors to perceive scene geometry and assume implicitly that the scene consists of relatively large, solid surfaces [7]. When driving in vegetated terrain, the notion of \obstacle" needs to be revisited. For example, a small bush can be considered an obstacle based solely on geometric speculation, although it probably can be driven over without damaging the vehicle. Thus, for ecient n a vigation in vegetated terrain, a higher level of reasoning must intervene, based on both the geometric description of the scene and the composition of the terrain cover.In this paper, we a r e i n terested in determining whether an \obstacle" is a rock (non-traversable) or a patch of grass (traversable). Terrain cover classication can be based on color features [1], but such a n a p p r o a c h w on't work at

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The widespread use of mobile devices is producing a huge amount of trajectory data, making the discovery of movement patterns possible, which are crucial for understanding human behavior. Significant advances have been made with regard to knowledge discovery, but the process now needs to be extended bearing in mind the emerging field of behavior informatics. This paper describes the formalization of a semantic-enriched KDD process for supporting meaningful pattern interpretations of human behavior. Our approach is based on the integration of inductive reasoning (movement pattern discovery) and deductive reasoning (human behavior inference). We describe the implemented Athena system, which supports such a process, along with the experimental results on two different application domains related to traffic and recreation management.

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