Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments

TL;DR

This paper introduces directional primitives, a probabilistic representation of road network directions and speeds, to improve uncertainty-aware motion prediction in urban driving scenarios using both simulated and real-world data.

Contribution

It proposes a novel representation of road network priors using von Mises and gamma distributions, enhancing motion estimation accuracy in urban environments.

Findings

Primitives improve uncertainty-aware motion estimation.

Effective in both simulated and real-world datasets.

Enhances prediction of vehicle behavior in complex scenarios.

Abstract

We can use driving data collected over a long period of time to extract rich information about how vehicles behave in different areas of the roads. In this paper, we introduce the concept of directional primitives, which is a representation of prior information of road networks. Specifically, we represent the uncertainty of directions using a mixture of von Mises distributions and associated speeds using gamma distributions. These location-dependent primitives can be combined with motion information of surrounding vehicles to predict their future behavior in the form of probability distributions. Experiments conducted on highways, intersections, and roundabouts in the Carla simulator, as well as real-world urban driving datasets, indicate that primitives lead to better uncertainty-aware motion estimation.