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 intersections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.
Context Aware Road-user Importance Estimation (iCARE)
2019 IEEE Intelligent Vehicles Symposium (IV) ; 2337-2343
2019-06-01
3575998 byte
Conference paper
Electronic Resource
English
CONTEXT AWARE ROAD-USER IMPORTANCE ESTIMATION (ICARE)
British Library Conference Proceedings | 2019
|British Library Conference Proceedings | 2010
|AIAA | 2010
|Online Contents | 1997
Solarflugzeuge - Icaré 2 gewinnt in Ulm den Berblinger-Wettbwerb
Online Contents | 1996