A big challenge regarding the development of autonomous vehicles is how to handle complex situations. If an autonomous vehicle ends up in a situation where it cannot make a decision on its own it will cause the car to stop, unable to continue driving. For these situations, human intervention is required. By making it possible to control the car remotely there is no need for an actual human in the car. Instead, a human operator can remotely control one or several cars from a distance. The purpose of this project was to identify such complex situations, evaluate remote control options and implement one of these controllers to drive the SVEA cars in the Smart Mobility Lab. After evaluation of possible remote control options, the PlayStation controller was chosen to be the simplest and most intuitive steering option. The controller was successfully implemented first in simulation and then on the SVEA cars in the Smart Mobility Lab. A test track was designed to measure the performance of the implemented controller and to be able to measure user-friendliness through a survey. It was concluded that a majority of the participants would not feel comfortable steering a real car using the PlayStation controller. However, a more extensive evaluation would be required to draw any major conclusions.

This paper explores the use of Naive Bayes as well a Linear Support Vector Machines in order to classify a text based on the level of medical emergency. The primary source of testing will be an online veterinarian service’s customer data. The aspects explored are whether a single text gives enough information for a medical decision to be made and if there are alternative data gathering processes that would be preferred. Past research has proven that text classifiers based on Naive Bayes and SVMs can often give good results. We show how to optimize the results so that important decisions can be made with these classifications as a basis. Optimal data gathering procedures will be a part of this optimization process. The business applications of such a venture will also be discussed since implementing such a system in an online medical service will possibly affect customer flow, goodwill, cost/revenue, and online competitiveness. / Denna studie utforskar användandet av Naive Bayes samt Linear Support Vector Machines för att klassificera en text på en medicinsk skala. Den huvudsakliga datamängden som kommer att användas för att göra detta är kundinformation från en online veterinär. Aspekter som utforskas är om en enda text kan innehålla tillräckligt med information för att göra ett medicinskt beslut och om det finns alternativa metoder för att samla in mer anpassade datamängder i framtiden. Tidigare studier har bevisat att både Naive Bayes och SVMs ofta kan nå väldigt bra resultat. Vi visar hur man kan optimera resultat för att främja framtida studier. Optimala metoder för att samla in datamängder diskuteras som en del av optimeringsprocessen. Slutligen utforskas även de affärsmässiga aspekterna utigenom implementationen av ett datalogiskt system och hur detta kommer påverka kundflödet, goodwill, intäkter/kostnader och konkurrenskraft.