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A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the drivers decisions and actions. Learning to estimate the importance of each object on the drivers real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.
This paper presents a new self-supervised system for learning to detect novel and previously unseen categories of objects in images. The proposed system receives as input several unlabeled videos of scenes containing various objects. The frames of th
Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detecti
Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. Most of the current methods detect road curbs online using vehicle-moun
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there i
Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric c