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.

中文翻译：

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城市环境中不确定性感知运动估计的定向原语

我们可以使用长时间收集的驾驶数据来提取有关车辆在不同道路区域的行为的丰富信息。在本文中，我们引入了方向基元的概念，它是道路网络先验信息的表示。具体来说，我们使用 von Mises 分布和使用伽马分布的相关速度的混合来表示方向的不确定性。这些与位置相关的原语可以与周围车辆的运动信息相结合，以概率分布的形式预测它们的未来行为。在 Carla 模拟器中对高速公路、十字路口和环形交叉路口以及真实世界的城市驾驶数据集进行的实验表明，原语可以实现更好的不确定性感知运动估计。