Jianda Chen

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East-ADL is an architectural description language dedicated to safety-critical automotive embedded system design. We have previously modified East-adl to include energy constraints and transformed energy-aware real-time behavioral constraints in East-adl into analyzable Uppaal models. In this paper, we extend our previous work by including support for(More)
In this paper we focus on what meaningful 2D perceptual information we can get from 3D LiDAR point cloud. Current work [1] [2] [3] have demonstrated that the depth, height and local surface normal value of a 3D data are useful features for improving Deep Neural Networks (DNNs) based object detection. We thus propose to organise LiDAR point as three(More)
The 3-D LiDAR scanner and the 2-D charge-coupled device (CCD) camera are two typical types of sensors for surrounding-environment perceiving in robotics or autonomous driving. Commonly, they are jointly used to improve perception accuracy by simultaneously recording the distances of surrounding objects, as well as the color and shape information. In this(More)
Relative attribute represents the correlation degree of one attribute between an image pair (e.g. one car image has more seat number than the other car image). While appearance highly and directly correlated relative attribute is easy to predict, fine-grained or appearance insensitive relative attribute prediction still remains as a challenging task. To(More)
Ancient paintings can provide valuable information for historians and archeologists to study the history and humanity of the corresponding eras. How to determine the era in which a painting was created is a critical problem, since the topic of a painting cannot be used as an effective basis without an era label. To address this problem, this article(More)
Vehicle detection at night time is of great importance for applications toward advanced driver assistance system. In this paper, we propose a method using deformable parts model for night time vehicle detection. Before detection, we use Nakagami distribution to find the regions of saliency. After that, we consider the regions in which pairs of regions of(More)
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