4,772 research outputs found
Robot graphic simulation testbed
The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
Identifying and remeshing contact interfaces in a polyhedral assembly for digital mock-up applications
Polyhedral models are widely used for applications such as manufacturing, digital simulation or visualization. They are discrete models; easy to store, to manipulate, allowing levels of resolution for visualization. They can be easily exchanged between CAD systems without loss of data. Previous works (Comput Aided Des 29(4):287–298, 1997, Comput Graphics 22(5):565–585, 1998) have focused on simplification process applied to polyhedral part models. The goal of the proposed approach is to extend these processes to polyhedral assembly models, describing the digital mock-up of a future manufacturing product. To apply simplification techniques or other processes on polyhedral assemblies, contact surfaces between interacting objects have to be identified and specific constraints must be applied for processing. The approach proposed allows checking and maintaining a global consistency of the assembly model to ensure the reliability of the future processes. Thus, contacts between objects are detected using an approach that works for a static configuration of the assembly. Finally, a precise detection of the faces involved in each contact area is made and the resulting input domains identified are processed using a local Frontal Delaunay re-meshing technique to produce an identical tessellation on both objects involved in the processed contact. The quality of the triangulation produced is also checked
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
Pedestrian intention recognition is very important to develop robust and safe
autonomous driving (AD) and advanced driver assistance systems (ADAS)
functionalities for urban driving. In this work, we develop an end-to-end
pedestrian intention framework that performs well on day- and night- time
scenarios. Our framework relies on objection detection bounding boxes combined
with skeletal features of human pose. We study early, late, and combined (early
and late) fusion mechanisms to exploit the skeletal features and reduce false
positives as well to improve the intention prediction performance. The early
fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for
pedestrian intention classification. Furthermore, we propose three new metrics
to properly evaluate the pedestrian intention systems. Under these new
evaluation metrics for the intention prediction, the proposed end-to-end
network offers accurate pedestrian intention up to half a second ahead of the
actual risky maneuver.Comment: 5 pages, 6 figures, 5 tables, IEEE Asilomar SS
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overcomes extreme illumination conditions and its low power
consumption makes it ideal for embedded systems on platforms such as drones and
self-driving cars. However, event-based data sets are scarce and labels are
even rarer for tasks such as object detection. We transferred discriminative
knowledge from a state-of-the-art frame-based convolutional neural network
(CNN) to the event-based modality via intermediate pseudo-labels, which are
used as targets for supervised learning. We show, for the first time,
event-based car detection under ego-motion in a real environment at 100 frames
per second with a test average precision of 40.3% relative to our annotated
ground truth. The event-based car detector handles motion blur and poor
illumination conditions despite not explicitly trained to do so, and even
complements frame-based CNN detectors, suggesting that it has learnt
generalized visual representations
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