Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

Traffic accidents are very serious problems for human life and the environment. In road safety, it is crucial to identify the high risk locations to apply proper counter measures. This paper aims at introducing outcomes of a pilot project whose main goal is to develop a GIS based crash analysis system integrated with the quantitative methods for identification of high risk locations on road networks in Turkey. In this concept, traditional hotspot detection methods used in Turkey(crash frequency, rate, and severity) are compared with the spatial statistical methods including Moran's I, GetisOrd G and planar and network kernel density estimation in terms of their sensitivity to spatial characteristics of crash clusters. Many countries use traditional hotspot detection approaches such as crash frequency, crash rate, and crash severity as well as Turkey. In this project, we aimed at obtaining a model including different hotspot identification methods for the safety program of Turkey. In order to obtain the model, many hotspot detection methods will be used and compare stage by stage. In the first stage, the seven methods mentioned above are used and examined. Although some of these methods are compared in couple, there is no study using all these methods together extensively in the literature. Methods validated with a different spatial vantage points. Repetitiveness of hotspots in a seven years period are used to compare the methods. Meanwhile advantages and disadvantages of the methods according to location of hotspots are examined additionally. Results show that using planar KDE with Gi in the junction locations and using planar KDE with Moran's I in the straight road locations could improve the model while determining hotspots.

The main goal of this study is to introduce two different approaches (i.e. based on spherical trigonometric formula and using sun qibla time) that can be used to determine the qibla directions of the existing mosques were oriented with...

In recent years, in addition to 2.4 GHz Wi-Fi signals, 5 GHz signals have been introduced making it possible to transmit data at both IEEE 802.11n and 802.11ac standards. Therefore, researchers have increasingly focused on developing indoor positioning applications based on Wi-Fi dual band. This study was conducted in two stages. In the first stage, we investigated the behaviours of 2.4 and 5 GHz Wi-Fi signals by collecting received signal strength (RSS) values from access points and determined the relationship between RSS and distance for each signal frequency using a curve fitting technique. Furthermore, we comparatively analysed signal fluctuations and their effects on positioning accuracy. In the second part of the study, we compared the positioning accuracy of four algorithms; namely, bilateration, trilateration, weighted iterative non-linear least square and extended Kalman filter using 2.4 and 5 GHz Wi-Fi signals. The experimental results revealed that the 5 GHz signals were more stable and had better positioning accuracy than the 2.4 GHz signals. Concerning the positioning algorithms, bilateration had the best positioning accuracy at both frequencies.