都市環境における不確実性を考慮した運動推定のための方向プリミティブ
Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments
道路網の事前情報を方向プリミティブとして表現し、周囲車両の将来挙動を確率分布として予測する手法を提案した論文。
著者: Ransalu Senanayake, Maneekwan Toyungyernsub, Mingyu Wang, Mykel J. Kochenderfer, Mac Schwager
分類: cs.RO, cs.AI, stat.AP
原文アブストラクト
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.
