  Future urban transportation concepts include a mixture of ground and air
vehicles with varying degrees of autonomy in a congested environment. In such
dynamic environments, occupancy maps alone are not sufficient for safe path
planning. Safe and efficient transportation requires reasoning about the 3D
flow of traffic and properly modeling uncertainty. Several different approaches
can be taken for developing 3D velocity maps. 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. On a collection of air and ground
datasets, we demonstrate that this approach is effective and more scalable than
several alternative approaches.
