This thesis achieved the goal of obtaining high accuracy rates (precision and recall) in a real-time system that detects and recognizes text in the automotive context. For the sake of simplicity, this work targets two Objects of Interest (OOIs): North American (NA) traffic boards (TBs) and license plates (LPs). The proposed approach adopts a hybrid detection module consisting of a Connected Component Analysis (CCA) step followed by a Texture Analysis (TA) step. An initial set of candidates is extracted by highlighting the Maximally Stable Extremal Regions (MSERs). Each sebsequent step in the CCA and TA steps attempts to reduce the size of the set by filtering out false positives and retaining the true positives. The final set of candidates is fed into a recognition stage that integrates an open source Optical Character Reader (OCR) into the framework by using two additional steps that serve the purpose of minimizing false readings as well as the incurred delays. A set of of manually taken videos from various regions of Ottawa were used to evaluate the performance of the system, using precision, recall and latency as metrics. The high precision and recall values reflect the proposed approach's ability in removing false positives and retaining the true positives, respectively, while the low latency values deem it suitable for the automotive context. Moreover, the ability to detect two OOIs of varying appearances demonstrates the flexibility that is featured by the hybrid detection module.

The automotive industry is facing a period of significant transformation due to the arrival of many new digital technologies. As the focus of automotive engineering has shifted from hardware to software, the conventional processes of making, buying and owning an automobile have changed. Peoples' desires for new automotive experiences are increasing; they demand more sophisticated approaches to the automotive experience beyond merely improving functional requirements for advanced automation systems, interfaces and connectivity. Thus, it is essential to understand human experience in order to help people deal with the high degree of complexity in the driving environment and to help them to cope with unanticipated driving events that involve emotional, psychological or sociological issues. This research takes a human-centred approach to investigating real-life scenarios in which people emotionally engage with automobiles with the aim of developing a relevant set of scenarios for this context. An extensive literature review was conducted of human emotion, memory systems, emotional memory characteristics, scenarios, and scenarios with emotional aspects, followed by a discussion defining scenario development process and affective scenarios. This research provides a methodology for in-depth qualitative studies that develop affective design scenarios with automobiles. As a triangulation approach, two independent studies in different settings explored affective scenario themes in automotive contexts of people's real-life car stories that made them respond emotionally. The themes that were revealed from both studies were consolidated, and exemplary scenarios of 13 consolidated main themes were formulated to illustrate a set of affective scenarios in automotive contexts. This research leads to an enhanced understanding of a set of critical contexts that automotive practitioners should take into account for future automotive design. Suggestions with possible questions based on the research outcome provide opportunities for them to agilely cope with unanticipated future events, whereby highly complex driving environment by connected and autonomous vehicles. This methodology used here can be replicated for future affective scenario studies focusing on specific products, sub-systems or services such as navigation systems or car-sharing services. The results, which have been validated through a triangulation approach, can bolster the automobile design process by addressing potential issues and challenges in automotive experience by facilitating idea generation, enhancing a shared understanding of critical contexts and by assisting decision-making among stakeholders from different departments.