3D Radar Velocity Maps for Uncertain Dynamic Environments
This addresses safety challenges in autonomous transportation systems, but it is incremental as it builds on existing Bayesian methods for mapping.
The paper tackles the problem of safe path planning in congested urban environments with mixed ground and air vehicles by developing 3D velocity maps that model uncertainty, demonstrating effectiveness and scalability on air and ground datasets.
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.