With the advent of communication age newer, faster and arguably better ways of moving information are at our disposal. People felt the need to stay connected which led to the evolution of smart gadgets like cell phones, tablets and laptops. The next generations of automobiles are keen in extending this connectivity to the vehicle user by arming themselves with radio interfaces. This move will enable the formation of vehicular networks where each car (mobile node) will be an instance of mobile ad hoc networks, popularly referred as Vehicular AdHoc Networks (VANETS). These networks will provide further necessary infrastructure for applications that can help improving safety and efficiency of road traffic as well as provide useful services for the mobile nodes (cars). The specific nature of VANETS brings up the need to address necessary security and privacy issues to be integrated into the social world. Thus, the open field of secure inter-vehicular communication promises an interesting research area. This thesis aims to quantify how much of a user trajectory can an adversary identify while monitoring non-safety applications in VANETS. Different types of adversaries, their attacks and possible non-safety applications are also discussed.

Advance Driver Assistance System (ADAS) are becoming the main issue in today’s automotive industry. The new generation of ADAS aims at focusing on more details and obtaining more accuracy. To achieve this objective, the research and development parts of the automobile industry intend to utilize Global Positioning System (GPS) by integrating it with other existing tools in ADAS. There are several driving assistance systems which are served by a digital map as a primary or a secondary sensor. The traditional techniques of digital map generation are expensive and time consuming and require extensive manual effort. Therefore, having frequently updated maps is an issue. Furthermore, the existing commercial digital maps are not highly accurate. This Master thesis presents several algorithms for automatically converting raw Universal Serial Bus (USB)-GPS and Real Time Kinematic (RTK) GPS traces into a routable road network. The traces are gathered by driving 20 times on a highway. This work begins by pruning raw GPS traces using four different algorithms. The first step tries to minimize the number of outliers. After the traces are smoothed, they tend to consolidate into smooth paths. So in order to merge all 20 trips together and estimate the road network a Trace Merging algorithm is applied. Finally, a Non-Uniform Rational B-Spline (NURBS) curve is implemented as an approximation curve to smooth the road shape and decrease the effect of noisy data further. Since the RTK-GPS receiver provides highly accurate data, the curve resulted from its GPS data is the most sufficient road shape. Therefore, it is used as a ground truth to compare the result of each pruning algorithm based on data from USB-GPS. Lastly, the results of this work are demonstrated and a quality evaluation is done for all methods.

The car is the most common vehicle in the world. Millions of cars are produced annually. In order for each car to find its buyer, car companies are forced to constantly improve the design of the car. Modern models are emerging, new car systems are being developed and implemented. All this is accompanied by a huge flow of information, in which it is easy to get lost. This master’s work is devoted to the trace analysis and connection of two different files. The paper proposes a developed algorithm of trace analysis for some functions of the vehicle in the C++ programming language. The files that we use to trace analysis relate to the model and the final result of its simulation.EATOP is a tool with which a model based on the EAST-ADL language was developed. Adapt is an event simulator with which our model of automotive functionality was simulated. The purpose of the study is to identify possible ways to meet timing requirements. The work is carried out in collaboration with Volvo Group Truck Technology. This company provided the LogFile, which presents results of the simulation, and model. We get an analysis of performance, one of the ways to trace data and timing. The results of our implementation are presented and discussed.