论文标题
在城市环境中的不确定性感知运动估计的定向原始图
Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments
论文作者
论文摘要
我们可以在很长一段时间内使用收集的驾驶数据来提取有关车辆在道路不同区域的行为方式的丰富信息。在本文中，我们介绍了定向原语的概念，该概念是道路网络先前信息的代表。具体而言，我们使用von mises分布和使用伽马分布的相关速度的混合物表示方向的不确定性。这些依赖位置的原始素可以与周围车辆的运动信息结合使用，以预测其未来行为，形式是概率分布的形式。在CARLA模拟器中的高速公路，交叉路口和回旋处进行的实验以及现实世界中的城市驾驶数据集，表明原语会导致更好的不确定性感知到感知的运动估计。
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.
