论文标题
在公路驾驶视频中的对象重要性估计的互动图
Interaction Graphs for Object Importance Estimation in On-road Driving Videos
论文作者
论文摘要
沿着道路行驶的车辆被许多物体包围，但只有一小部分影响驾驶员的决定和行动。学会估计每个对象对驾驶员实时决策的重要性可能有助于更好地理解人类驾驶行为并导致更可靠的自主驾驶系统。解决此问题需要了解自我车辆与周围物体之间的相互作用的模型。但是，场景中其他物体之间的互动也可能非常有帮助，例如，一个行人开始穿越自我车辆和前面的汽车之间的道路将使汽车在前面的重要性不大。我们建议使用相互作用图提出一个新颖的框架，用于对象重要性估算，其中每个对象节点的特征通过通过图形卷积与他人进行交互来更新。实验表明，我们的模型优于最先进的基线，其输入和预处理要少得多。
A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions. Learning to estimate the importance of each object on the driver's real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.
