Utilizing a Global Navigation Satellite System (GNSS) together with an Inertial Navigation System (INS) is today a common integration method to obtain a positioning solution for autonomous systems. Both GNSS and INS have benefits and weaknesses where the best parts from both systems can be combined with a Kalman filter. Because of this complementary nature, it is of interest to look at the robustness of the positioning solution when the Global Navigation Satellite System is temporarily not available. The aim of the thesis has been to investigate different vehicle models and to evaluate their short-time performance using a Dead Reckoning approach. The goal has been to develop a system for a Heavy Duty Vehicle (HDV) and to find out for which time interval a specific model can stay within a certain range when the GNSS is lost. A GNSS outage could for example happen when driving on a highway and passing signs, bridges and especially when driving inside tunnels. Also, for a solution to become commercially interesting, it must be cheap. Therefore, is it common to use so called Micro-Electro-Mechanical-Systems (MEMS) sensors which are of low-cost but suffer from biases, scale factors and temperature dependencies which must be compensated for. The results from the tests show that some models are able to estimate the position with good precision during short time GNSS outages whereas other models do not deliver the required accuracy. The main conclusion is that care should be taken when choosing the vehicle model so that it fits its usage area and the complexity needed to describe its motion. There are also lots of parameters to look at when investigating the best solution, where modeling of the low-cost sensors is one of them.

By using vehicle-to-vehicle communication, vehicles can cooperate in many waysby sending positions and other relevant data between each other. One popularexample is platooning where many, especially heavy vehicles, drive on a trailwith short distances resulting in a reduction of air resistance. To achieve a goodefficiency of the platooning it is required that vehicle fleets are coordinated, sothat the percentage of time for driving in platoon is maximized without affectingthe total driving time and distance too much. For large fleets, this is a complexoptimization problem which would be difficult to solve by only using the realworld as the test environment. To provide a more adaptable test environment for the communication and platooningcoordination, an augmented reality with virtual vehicles (“Ghost trucks")with relevant communication abilities are developed. In order to realise the virtualtesting environment for trucks, Scania initiated a project that could be dividedinto the workload of three master theses. This thesis involved the part ofdeveloping the virtual vehicles, which include the development of a truck modeland a driver model. The developed truck model consists of a single track vehicle model and severalpowertrain models of different complexity provided by Scania. Additionally, thedriver model consists of steering wheel and speed controls in order to keep thetruck on a safe distance from the lead truck and stay on a preferred lane. The keyfeature of the driver-truck model is its modular design, which provides great flexibilityin selecting the level of detail for each component. The driver-truck modelcan be duplicated and simulated together in real time and performs platooningwith each other in a road system based on the real world. As the driver-truckmodel is module based, it can easily be extended for future purposes with morecomplex functions. The driver-truck model is implemented in Simulink and the simulation performancefor different model complexity is evaluated. It is demonstrated that theflexibility of the developed model allows a balanced decision to be made betweenrealistic truck behavior and simulation speed. Furthermore, multi-truck simulationsare performed using the model, which demonstrate the effectiveness of themodel in the evaluation of truck platooning operations.