# 3D Radar Velocity Maps for Uncertain Dynamic Environments

arXiv: https://arxiv.org/abs/2107.11039 · HTML: https://mlsanity.com/paper/2107.11039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning](https://mlsanity.com/paper/1908.08729/) (2019)
- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Gaussian Processes for Data-Efficient Learning in Robotics and Control](https://mlsanity.com/paper/1502.02860/) (2015)
- [Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning](https://mlsanity.com/paper/1904.12374/) (2019)
- [Efficient Large-Scale Multi-Drone Delivery Using Transit Networks](https://mlsanity.com/paper/1909.11840/) (2019)
- [Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environments](https://mlsanity.com/paper/2011.09045/) (2020)
- [Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping](https://mlsanity.com/paper/2009.07207/) (2020)
- [Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments](https://mlsanity.com/paper/2007.00161/) (2020)

## Abstract (arXiv)

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.