Eco-driving assistant application
Abstract: The global environmental impact made by cars is substantial and will always be in focus when discussing climate change and our carbon footprint. Eco-driving has long been a given set of general rules for drivers to follow such as smooth acceleration. We wanted to explore the use of machine learning to identify and learn unknown driving patterns that may affect the fuel consumption. We also wanted to explore how communication between the driver and the application should be designed so that it doesn't disturb the driver while driving and how guidelines should be illustrated to motivate the driver in driving eco-friendlier. We did it by exploring the use of gamification as a motivational tool. We investigated different machine learning techniques and explored each method's limitations and possibilities. We also tested different design alternatives for the application to see which design is both suitable for driving and motivates the driver to reach their eco-goal. We found that there are many ways to apply machine learning for eco-driving purposes and each method has its own set of pros and cons. In this report we provide no single right answer to how to apply gamification and machine learning in a driving environment but rather a proof of concept to follow for further development. Data-driven development has many applications in real-world problems and eco-driving is one of them. We learned that personalizing feedback and displaying it with a gamified design encouraged drivers to be motivated into reaching an eco-friendlier driving style.
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