Despite numerous investments toward autonomous vehicle technology this past decade the ensured safe operation of these systems is still an unresolved issue for both commercial and defense systems due to decision uncertainty. In complex dynamic domains (e.g. intersections or congested terrain) the expected mode of operation for ensured safety of these unmanned systems is still direct human control (whether through direct vehicle input or through teleoperation). This paper presents research toward an autonomous vehicle safety reasoning system that provides a novel approach to temporally address scene uncertainty to increase the safety envelope for commercial and defense systems.
Over the last two decades, research in Unmanned Vehicles (UV) has rapidly progressed and become more influenced by
the field of biological sciences. Researchers have been investigating mechanical aspects of varying species to improve
UV air and ground intrinsic mobility, they have been exploring the computational aspects of the brain for the
development of pattern recognition and decision algorithms and they have been exploring perception capabilities of
numerous animals and insects. This paper describes a 3 month exploratory applied research effort performed at the US
ARMY Research, Development and Engineering Command's (RDECOM) Tank Automotive Research, Development
and Engineering Center (TARDEC) in the area of biologically inspired spectrally augmented feature selection for robotic
visual odometry. The motivation for this applied research was to develop a feasibility analysis on multi-spectrally
queued feature selection, with improved temporal stability, for the purposes of visual odometry. The intended
application is future semi-autonomous Unmanned Ground Vehicle (UGV) control as the richness of data sets required to
enable human like behavior in these systems has yet to be defined.
KEYWORDS: LIDAR, Sensors, Roads, Robotics, Global Positioning System, Control systems, Cameras, Safety, Unmanned ground vehicles, Commercial off the shelf technology
The 2005 DARPA Grand Challenge (DCG) was a 'Huge Leap Forward for Robotics R&D' according to the DARPA
Grand Challenge tracking website. Similar to the transatlantic flight competition that spurred commercial flights all
over the world, the Grand Challenge was a step forward in the area of navigation for unmanned ground vehicles.
However, questions like 'What are the important technologies brought forth by the Grand Challenge?' and 'How can
these technologies assist our soldiers in the field?' need to be addressed. This paper will look at the 2005 DARPA
Grand Challenge from the perspective of individuals involved in some of the Army's unmanned ground vehicle
programs. Information will be presented contrasting this year's competition to the one held in 2004. Details of the
enabling technologies from many of the competitors will be discussed along with problems they encountered at the
National Qualification Event (NQE) and on Race Day. Finally, thoughts will be presented on how these technologies
may be harvested in commercial and DOD research and development for current and future systems.
In this paper, methods of choosing a vehicle out of an image are explored. Digital images are taken from a monocular camera. Image processing techniques are applied to each single frame picture to create the feature vector. Finally the resulting features are used to classify whether there is a car in the picture or not using support vector machines. The results are compared to those obtained using a neural network. A discussion on techniques to enhance the feature vector and the results from both learning machines will be included.
Support Vector Machines (SVMs) have become popular due to their accuracy in classifying sparse data sets. Their computational time can be virtually independent of the size of the feature vector. SVMs have been shown to out perform other learning machines on many data sets. In this paper, we use SVMs to detect a car in a lane of traffic. Digital pictures of various driving situations are used. The results from the SVM algorithm are compared to results from a standard neural network approach.
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