Road-users are a critical part of decision-making for both self-driving cars
and driver assistance systems. Some road-users, however, are more important for
decision-making than others because of their respective intentions, ego
vehicle's intention and their effects on each other. In this paper, we propose
a novel architecture for road-user importance estimation which takes advantage
of the local and global context of the scene. For local context, the model
exploits the appearance of the road users (which captures orientation,
intention, etc.) and their location relative to ego-vehicle. The global context
in our model is defined based on the feature map of the convolutional layer of
the module which predicts the future path of the ego-vehicle and contains rich
global information of the scene (e.g., infrastructure, road lanes, etc.), as
well as the ego vehicle's intention information. Moreover, this paper
introduces a new data set of real-world driving, concentrated around
inter-sections and includes annotations of important road users. Systematic
evaluations of our proposed method against several baselines show promising
results.Comment: Published in: IEEE Intelligent Vehicles (IV), 201