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Background: With the emerging application of autonomous vehicles in the automotive industry, several efforts have been made for the complete adoption of autonomous vehicles. One of the several problems in creating autonomous technology is the detection of water puddles, which can cause damages to internal components and the vehicle to lose control. This thesis focuses on the detection of water puddles on-road and off-road conditions with the use of Deep Learning models. Objectives: The thesis focuses on finding suitable Deep Learning algorithms for detecting the water puddles, and then an experiment is performed with the chosen algorithms. The algorithms are then compared with each other based on the performance evaluation of the trained models. Methods: The study uses a literature review to find the appropriate Deep Learning algorithms to answer the first research question, followed by conducting an experiment to compare and evaluate the selected algorithms. Metrics used to compare the algorithm include accuracy, precision, recall, f1 score, training time, and detection speed. Results: The Literature Review indicated Faster R-CNN and SSD are suitable algorithms for object detection applications. The experimental results indicated that on the basis of accuracy, recall, and f1 score, the Faster R-CNN is a better performing algorithm. But on the basis of precision, training time, and detection speed, the SSD is a faster performing algorithm. Conclusions: After carefully analyzing the results, Faster R-CNN is preferred for its better performance due to the fact that in a real-life scenario which the thesis aims at, the models to correctly predict the water puddles is key

Software development is an area in which companies not only need to keep up with the latest technology, but they additionally need to continuously increase their productivity to stay competitive in the industry. One company currently facing these challenges is Storytel - one of the strongest players on the Swedish audiobook market - with about a fourth of all employees involved with software development, and a rapidly growing workforce. With the purpose of understanding how the Storytel Tech Department is performing, this thesis maps Storytel’s productivity defined through the Four Key Metrics - Deployment Frequency, Delivery Lead Time, Mean Time To Restore and Change Fail Rate. A classification is made into which performance category (Low, Medium, High, Elite) the Storytel Tech Department belongs to through a deep-dive into the raw system data existing at Storytel, mainly focusing on the case management system Jira. A survey of the Tech Department was conducted, to give insights into the connection between human and technical factors influencing productivity (categorized into Culture, Environment, and Process) and estimated productivity. Along with these data collections, interviews with Storytel employees were performed to gather further knowledge about the Tech Department, and to understand potential bottlenecks and obstacles. All Four Key Metrics could be determined based on raw system data, except the metric Mean Time To Restore which was complemented by survey estimates. The generalized findings of the Four Key Metrics conclude that Storytel can be minimally classified as a ‘medium’ performer. The factors, validated through factor analysis, found to have an impact on the Four Key Metrics were Generative Culture, Efficiency (Automation and Shared Responsibility) and Number of Projects. Lastly, the major bottlenecks found were related to Architecture, Automation, Time Fragmentation and Communication. The thesis contributes with interesting findings from an expanding, middle-sized, healthy company in the audiobook streaming industry - but the results can be beneficial for other software development companies to learn from as well. Performing a similar study with a greater sample size, and additionally enabling comparisons between teams, is suggested for future research.