32,535 research outputs found
Quantitative Information Flow as Safety and Liveness Hyperproperties
We employ Clarkson and Schneider's "hyperproperties" to classify various
verification problems of quantitative information flow. The results of this
paper unify and extend the previous results on the hardness of checking and
inferring quantitative information flow. In particular, we identify a subclass
of liveness hyperproperties, which we call "k-observable hyperproperties", that
can be checked relative to a reachability oracle via self composition.Comment: In Proceedings QAPL 2012, arXiv:1207.055
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
Understanding ego-motion and surrounding vehicle state is essential to enable
automated driving and advanced driving assistance technologies. Typical
approaches to solve this problem use fusion of multiple sensors such as LiDAR,
camera, and radar to recognize surrounding vehicle state, including position,
velocity, and orientation. Such sensing modalities are overly complex and
costly for production of personal use vehicles. In this paper, we propose a
novel machine learning method to estimate ego-motion and surrounding vehicle
state using a single monocular camera. Our approach is based on a combination
of three deep neural networks to estimate the 3D vehicle bounding box, depth,
and optical flow from a sequence of images. The main contribution of this paper
is a new framework and algorithm that integrates these three networks in order
to estimate the ego-motion and surrounding vehicle state. To realize more
accurate 3D position estimation, we address ground plane correction in
real-time. The efficacy of the proposed method is demonstrated through
experimental evaluations that compare our results to ground truth data
available from other sensors including Can-Bus and LiDAR
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