Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments
This work addresses motion prediction for autonomous vehicles in urban settings, but it appears incremental as it builds on existing uncertainty modeling methods.
The paper tackled the problem of predicting vehicle behavior in urban environments by introducing directional primitives, a representation of prior road network information using von Mises and gamma distributions, and showed that this approach improves uncertainty-aware motion estimation in simulations and real-world datasets.
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.