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Future urban transportation concepts include a mixture of ground and airvehicles with varying degrees of autonomy in a congested environment. In suchdynamic environments, occupancy maps alone are not sufficient for safe pathplanning. Safe and efficient transportation requires reasoning about the 3Dflow of traffic and properly modeling uncertainty. Several different approachescan be taken for developing 3D velocity maps. This paper explores a Bayesianapproach that captures our uncertainty in the map given training data. Theapproach involves projecting spatial coordinates into a high-dimensionalfeature space and then applying Bayesian linear regression to make predictionsand quantify uncertainty in our estimates. On a collection of air and grounddatasets, we demonstrate that this approach is effective and more scalable thanseveral alternative approaches.

3D Radar Velocity Maps for Uncertain Dynamic Environments