We formulate a new problem as Object Importance Estimation (OIE) in on-road
driving videos, where the road users are considered as important objects if
they have influence on the control decision of the ego-vehicle's driver. The
importance of a road user depends on both its visual dynamics, e.g.,
appearance, motion and location, in the driving scene and the driving goal,
\emph{e.g}., the planned path, of the ego vehicle. We propose a novel framework
that incorporates both visual model and goal representation to conduct OIE. To
evaluate our framework, we collect an on-road driving dataset at traffic
intersections in the real world and conduct human-labeled annotation of the
important objects. Experimental results show that our goal-oriented method
outperforms baselines and has much more improvement on the left-turn and
right-turn scenarios. Furthermore, we explore the possibility of using object
importance for driving control prediction and demonstrate that binary brake
prediction can be improved with the information of object importance.

本文针对车载自动驾驶视觉场景中的对象重要性评估问题，提出了一种新的基于视觉动态和驾驶目标的对象重要性估计框架，并通过实际交通路口数据集及人工标注数据对该框架进行了验证评估。同时，本文还探讨了使用对象重要性进行驾驶控制预测的可能性，并证明了该方法可以有效提升车辆的刹车预测准确性。