In this talk we will look at a new algorithmic solution mitigating the effect of "range-anxiety" of electric vehicle owners, i.e. the fear of running out of the battery. We first employ automatic machine learning to learn how different drivers affect consumption of an electric car. We then use this knowledge to design a new efficient graph-planning algorithm to predict effective drivable range tailored for individual drivers.
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Algorithms for intelligent vehicles
Peter is a PhD student at Oxford University Mobile Robotics Group and has a strong background in algorithm design. His primary focus is on applications of Artificial Intelligence in robotics and his further research interests span areas as diverse as deep machine learning, inverse reinforcement learning, computer vision, planning and natural language processing. Peter has done internships in Facebook, Google, Microsoft Research and MetaMind. In his free time he enjoys participation in algorithmic programming competitions like TopCoder and Codeforces where he enjoys notable success.