Context Aware Road-user Importance Estimation (iCARE)
This work addresses the need for better decision-making in self-driving cars and driver assistance systems by focusing on identifying critical road-users, though it appears incremental as it builds on existing context-aware methods.
The paper tackles the problem of estimating road-user importance for autonomous vehicles by proposing a novel architecture that uses local and global context, and introduces a new real-world driving dataset with annotations, showing promising results in evaluations against baselines.
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.