An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.<p>This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.

The automotive industry is actively supporting research and innovation to meet manufacturers' requirements related to safety issues, performance and environment. The Green ITS project is among the efforts in that regard. Safety is a major customer and manufacturer concern. Therefore, much effort have been directed to developing cutting-edge technologies able to assess driver status in term of alertness and suitability. In that regard, we aim to create with this thesis a framework for a context-aware driver status assessment system. Context-aware means that the machine uses background information about the driver and environmental conditions to better ascertain and understand driver status. The system also relies on multiple sensors, mainly video and audio. Using context and multi-sensor data, we need to perform multi-modal analysis and data fusion in order to infer as much knowledge as possible about the driver. Last, the project is to be continued by other students, so the system should be modular and well-documented. With this in mind, a driving simulator integrating multiple sensors was built. This simulator is a starting point for experimentation related to driver status assessment, and a prototype of software for real-time driver status assessment is integrated to the platform. To make the system context-aware, we designed a driver identification module based on audio-visual data fusion. Thus, at the beginning of driving sessions, the users are identified and background knowledge about them is loaded to better understand and analyze their behavior. A driver status assessment system was then constructed based on two different modules. The first one is for driver fatigue detection, based on an infrared camera. Fatigue is inferred via percentage of eye closure, which is the best indicator of fatigue for vision systems. The second one is a driver distraction recognition system, based on a Kinect sensor. Using body, head, and facial expressions, a fusion strategy is employed to deduce the type of distraction a driver is subject to. Of course, fatigue and distraction are only a fraction of all possible drivers' states, but these two aspects have been studied here primarily because of their dramatic impact on traffic safety. Through experimental results, we show that our system is efficient for driver identification and driver inattention detection tasks. Nevertheless, it is also very modular and could be further complemented by driver status analysis, context or additional sensor acquisition.

Increasing  environmental  pressure  makes  it  significantly  important  to  improve  the share  of  renewable  energy  source  in  terms  of  sustainable  development.  Photovoltaic  (PV)  cells are one of the most promising technologies at present for utilizing solar radiation. However,  the large  scale  of  PV  penetration  with  its  character  of  intermittency  may  cause  problems  for  the power system and requires a more complex power system control. Self-consumption is a feasible solution to reduce the negative impact of PV on the power system. On the other hand, Plugged-in electric vehicle which could get charged by the electricity from the grid is a potential load for the general household in the future since the introduction of electric vehicles (EVs) is critical for building  a  fossil-fuel  independent  transportation.  The  aim  of  the  project  is  to  investigate  the effect on the power consumption profile when adding PV generation and electric vehicle load, as well  as  whether  the  introduction  of  electric  vehicle  will  help  improve  the  matching  between electricity consumption and PV generation. This study is done on both an individual household scale and a national scale. Conclusion from the simulation is that home-charged EV accounts for a  great  deal  of  energy  consumption  for  a  single  household  and  it  could  improve  the  national energy  consumption  to  some  extent  if  largely  introduced  into  the  power  system.  In  addition, Home-charged EV without strategic control does not improve self-consumption of PV either for a single household or on a national scale. / <p></p><p></p>

One area of vehicle handling performance that has been the focus of an OEM{'}s (Original Equipment Manufacturer) engineering effort is within the realm of vehicle straight-line performance. As the name implies, straight-line performance is determinant on the vehicle{'}s tendency to resist vehicle lateral drift when being driven straight. Vehicle lateral drift is a condition where the driver must apply a constant correctional torque to the steering wheel in order to maintain a straight line course. A full vehicle model was developed to simulate the influences of suspension parameters on vehicle drift. Adams 2010 was chosen as the multi-body dynamics (MBD) software for this research for its ability to develop a full vehicle high fidelity model without the need for physical test data. The model was created from standard Adams/Car suspension templates modified to accommodate the subject vehicle. The front suspension sub-assembly model was built upon the front MacPherson strut suspension template. Likewise, the rear suspension sub-assembly model was created from the rear multi-link suspension template. The tire model used in the full vehicle model was based on the Pacejka 2002 formulation. A model of a similar tire was generated using a custom spreadsheet based on the PAC2002, a slightly modified version of the Pacejka 2002 formulation found within Adams/Car. A virtual tire test rig and a 6/7-DoF model were created to understand and verify the behaviour of the generated tire models. The virtual tire test rig was used to compare the outputs of the PAC2002 tire model to the calculated values from a custom tire property spreadsheet. The 6/7-DoF model was used to test and verify the effect of the tire{’}s residual lateral forces. The full-vehicle model was verified using the parallel wheel travel and opposite wheel travel suspension analyses. The parallel wheel travel analysis was used to tease out binding issues within the designed travel of the suspension. The opposite wheel travel analysis was used similarly for anti-roll bar systems. Simulations based on the industry standard vehicle drift tests were run to understand the effect of certain vehicle suspension geometry on vehicle drift, namely the vehicle{’}s front and rear camber and toe angles. The full-vehicle model was also subjected to straight-line performance simulations with various road bank or crown angles. The results were compared with industry-standard vehicle drift test data gathered by the OEM on their own test track. The results indicate that the direction of vehicle pull matches with the OEM test data, but the magnitudes differ in both the positively and negatively banked road simulation results. It is likely that the difference in vehicle drift is due to the lack of steering data obtained for the full-vehicle model.

In this project the optimization of transport planning has been studied. The approach was that smaller transport companies do not have the capability to fully optimize their transports. Their transport optimization is performed at a company level, meaning that the end result might be optimal for their company, but that potential for further optimization exists. The idea was to build a collaboration of transport companies, and then to optimize the transports globally within the collaboration. The intent was for the collaboration to perform the same driving assignments but at a lower cost, by using fewer vehicles and drivers, or travel shorter distance, or both combined. This should be achieved by planning the assignments in a smarter way, for example using a company's empty return journey to perform an assignment for another company. Due to the complexity of these types of problems, called Vehicle Routing Problem (VRP), shown to be NP-complete, search methods are often used. In this project the method of choice was a PDDL-based planner called LPG-td. It uses enforced hill-climbing together with a best-first search to find feasible solutions. The method was tested for scaling, performance versus another method and against time, as well as together with a real-life based problem. The results showed that LPG-td might not be a suitable candidate to solve the problem considered in this project. The solutions found for the collaboration were worse than for the sum of individual solutions, and used more computational time. Since the solution for the collaboration at most should be equal to the sum of individual solutions, in theory, this meant that the planner failed.

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In an advanced avionics system, the demand of high reliability and availability is of great importance. Testability Analysis is a method of examining this. In the project RWE Tornado GE at Saab Avitronics, they use Built-In Test (BIT), for the purpose of detecting and isolating possible failures in the equipment in question. There is however the need of verification of BIT functionality. Some of the verification tests are requested by customer EADS to be simulated and demonstrated. The objective of this thesis is to understand the Testability Analysis process as well as develop a tool to assist the Testability Demonstration preparations and result recording. / Kravet på hög tillförlitlighet samt tillgänglighet är av yttersta vikt inom ett avancerat avioniksystem. Testbarhetsanalys är en undersökningsmetod som man kan tillämpa. I projektet RWE Tornado GE på Saab Avitronics använder man sig av inbyggda tester - Built-In Test (BIT) - för att kunna upptäcka och isolera eventuella felmoder i en utrustning. Utöver detta vill man verifiera att BIT-funktionen faktiskt fungerar. Vissa av dessa verifieringstester måste simuleras och demonstreras i enlighet med kunden EADS begäran. Målet med examensarbetet är att förstå testbarhetsanalysprocessen samt utveckla ett verktyg för att kunna göra nödvändiga förberedelser för samt resultatinsamling under testbarhetsdemonstrationen (T-Demo).

Since 1970, vehicle’s exhaust pollutions have received increasing attention as a source of air pollution at both local (human health concerns) and global (global warming) scales. This study mainly discusses diesel and gasoline vehicles because, today, over 90% of vehicles on the road use gasoline and diesel fuels. The major concerns of gasoline exhaust contaminants are carbon monoxide (CO), hydrocarbon (HC), carbon dioxide (CO2) and polycyclic aromatic hydrocarbons (PAHs); the major concerns of diesel exhaust emissions contaminants are nitrogen oxides (NOX) and particulate matter (PM). The aim of this study is mainly to compare gasoline and diesel fuels, and to determine which fuel and its developed forms are less harmful to humans, and which are most suitable for the natural environment at both a local and global level. The results show that burning gasoline fuels will emit less PM and NOX emissions than burning diesel fuels, but it will generate about 50% more CO2 than diesel fuels, and it also emit about ten times more CO, PAHs and around five times more HC than diesel fuels; burning diesel fuels will produce less CO2 emissions than gasoline fuels, but will emit around ten times more NOX and PM than gasoline fuels. Consequently using a gasoline car in urban areas might help to reduce the human health effects; using a diesel car on motorways or in rural areas might help to reduce the greenhouse gas (GHG) emissions and minimize the global warming effects. Biofuels, biodiesel and ethanol, have the potential to minimize the vehicle exhaust emissions and adverse effects. Nonetheless, there are still many debatable issues around biodiesel, such as insufficient fuel supply and health concerns (especially, ultrafine particles (UFPs)). In the future, there remains a need to continue the further studies of vehicle exhaust emissions, and to improve the understanding of all vehicle exhaust emissions and all of their impacts, especially the vehicle exhaust health research.

This thesis work was undertaken to develop a new high level command for an unmanned aerial vehicle. The command is assumed to make the UAV follow a reference position that is placed on a certain distance to an object. At the same time the UAV is assumed to move more smoothly than the reference position and the UAV is allowed to follow the reference position with margin. The problem was solved with an automatic control system that takes the reference position as input signal and has a fictitious position as output signal. The fictitious position moves smoothly inside the margin and irregular behavior of the reference position is smoothed out by the automatic control system. The fictitious position is affected by strong feedback outside the margin and weak feedback inside the margin. This makes the fictitious position to stay inside the margin and moves smoothly inside the margin. The UAV follows the fictitious position instead of the reference position. In this way the UAV holds a certain distance to an object and at the same time moves smoother than the object.

An autonomous machine is a machine that can navigate through its environment without human interactions. These machines use sensors to sense the environment and have computing abilities for receiving and interpreting the sensory data as well as for controlling their displacement. At the University of Saskatchewan (Saskatoon, Canada), a low level autonomous machine was developed. This low level machine was the sensor system for an autonomous machine. The machine was capable of sensing the environment and carrying out actions based on commands sent to it. This machine provided a sensing and control layer, but the path planning (decision making) part of the autonomous machine was not developed.<p>This autonomous machine was developed on a Case IH DX 34H tractor with the purpose of providing a machine for testing software and sensors in a true agricultural environment. The tractor was equipped with sensors capable of sensing the speed and heading of the tractor. A control architecture was developed that received input commands from a human or computer in the form of a target heading and speed. The control architecture then adjusted controls on the tractor to make the tractor reach and maintain the target heading and speed until a new command was provided. The tractor was capable of being used in all kinds of weather, although some minor issues arose when testing in rain and snow. The sensor platform developed was found to be insufficient for proper control. The control structure appeared to work correctly, but was hindered by the poor sensor platform performance.