Context Aware Roaduser Importance Estimation (iCARE) Roadusers are a critical part of decisionmaking for both selfdriving cars and driver assistance systems. Some roadusers, however, are more important for decisionmaking than others because of their respective intentions, egovehicleu0027s intention and their effects on each other. In this paper, we propose a novel architecture for roaduser 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 egovehicle. 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 egovehicle and contains rich global information of the scene (e.g., infrastructure, road lanes, etc.), as well as the egovehicleu0027s intention information. Moreover, this paper introduces a new data set of realworld driving, concentrated around intersections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.

Context and Intention Aware Planning for Urban Driving We present a novel autonomous driving system which uses the road contextual information and intentions of other road users for urban driving. Unlike highways, urban environments require the drivers to follow traffic signs and signals while using their best judgment for anomalous situations. In such scenarios, a selfdriving car needs to understand and take into account the uncertainties in the environment to plan and decide its action accordingly. Our planner models the intentions of the surrounding vehicles leveraging a neural network, and integrates the road contextual information to reduce its environment uncertainties and also speed up the decision making process. We validate our planner in simulation and in a real urban environment. Our experimental results show that integrating intention inference and road contextual information for prediction, planning and decision making help improve safety and efficiency of our autonomous driving system.