论文标题
自动驾驶日志时空标记的通用嵌入
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
论文作者
论文摘要
在本文中，我们解决了原始传感器数据中自动驾驶场景的时空标记问题。我们的方法学会了所有标签的通用嵌入，从而有效地对许多属性进行了有效的标记，并使用有限的数据来更快地学习新属性。重要的是，嵌入是时空意识到的，使该模型可以自然输出时空标签值。然后可以将值汇合在任意区域上，以便例如计算SDV前面的行人密度，或确定汽车在4向交叉路口处阻塞了另一辆车。我们证明了方法在新的大型自动驾驶数据集（SDVSCENES）上的有效性，其中包含与车辆和行人密度有关的15个属性，每个参与者的作用，每个参与者的速度，参与者之间的相互作用，以及路线图的拓扑。
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data. Importantly, the embedding is spatio-temporally aware, allowing the model to naturally output spatio-temporal tag values. Values can then be pooled over arbitrary regions, in order to, for example, compute the pedestrian density in front of the SDV, or determine if a car is blocking another car at a 4-way intersection. We demonstrate the effectiveness of our approach on a new large scale self-driving dataset, SDVScenes, containing 15 attributes relating to vehicle and pedestrian density, the actions of each actor, the speed of each actor, interactions between actors, and the topology of the road map.
