Future urban transportation concepts include a mixture of ground and air vehicles with varying degrees of autonomy in a congested environment . Safe and efficient transportation requires reasoning about the 3Dflow of traffic and properly modeling uncertainty . This paper explores a Bayesian approach that captures our uncertainty in the map given training data . The approach involves projecting spatial coordinates into a high-dimensional feature space and then applying Bayesian linear regression to make predictions and quantify uncertainty in our estimates . This approach is effective and more scalable than several alternative approaches, we demonstrate that this approach is more effective than other approaches .

Author(s) : Ransalu Senanayake, Kyle Beltran Hatch, Jason Zheng, Mykel J. Kochenderfer

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Keywords : approach - uncertainty - transportation - bayesian - effective -

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