scholarly journals Fusion of range measurements and semantic estimates in an evidential framework / Fusion von Distanzmessungen und semantischen Größen im Rahmen der Evidenztheorie

2019 ◽  
Vol 86 (s1) ◽  
pp. 102-106
Author(s):  
Sven Richter ◽  
Sascha Wirges ◽  
Hendrik Königshof ◽  
Christoph Stiller
Keyword(s):  
Data Processing ◽  
Robotic Systems ◽  
Automated Driving ◽  
Information Redundancy ◽  
Distance Measurements ◽  
Image Domain ◽  
Crucial Component ◽  
Scene Representation ◽  
Efficient Data ◽  
Real Traffic

AbstractA detailed reconstruction of the environment is a crucial component of mobile robotic systems and enables higher level scene understanding. To achieve information redundancy heterogenous sensors need to be used with each sensor having specific strengths and weaknesses. Therefore, the goal of this work is to fuse information from multiple lidars, radars, a stereo camera and semantic camera information into one common scene representation. In contrast to past publications, we focus on the combination of distance measurements and semantic estimates in the image domain in one common evidential framework. Grid maps are used as common fusion structure which enable efficient data processing. The approach is validated on an automated driving plattform in real traffic scenarios. Experiments show that the scene reconstruction precision increases while still retaining the real-time capability.

scholarly journals Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

2021 ◽  
Vol 11 (21) ◽  
pp. 10166
Author(s):  
Leonard Stepien ◽  
Silvia Thal ◽  
Roman Henze ◽  
Hiroki Nakamura ◽  
Jacobo Antona-Makoshi ◽  
...  
Keyword(s):  
Safety Evaluation ◽  
Driving Safety ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Complex Environments ◽  
Automated Driving ◽  
Evaluation Approach ◽  
Naturalistic Driving ◽  
Real Traffic

Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios.

Energy-Efficient Data Processing at Sweet Spot Frequencies

2014 ◽  
pp. 154-171 ◽  
Author(s):  
Sebastian Götz ◽  
Thomas Ilsche ◽  
Jorge Cardoso ◽  
Josef Spillner ◽  
Uwe ASSmann ◽  
...  
Keyword(s):  
Data Processing ◽  
Energy Efficient ◽  
Sweet Spot ◽  
Efficient Data

Database System for the Virtual Collection

2015 ◽  
pp. 180-199
Keyword(s):  
Data Processing ◽  
Developmental Process ◽  
Information Age ◽  
Database Systems ◽  
Computing Systems ◽  
Electronic Computing ◽  
Database Technology ◽  
Efficient Data ◽  
The Times ◽  
The Many

Database technology is highly developed for the many uses that it is employs; although, tomorrow will hold new challenges and demands that it is ill-equipped to accomplish. The rigors and demands of the current Information Age pushes information systems to develop more universal solutions not pre-established on the proprietary demands of capitalistic conceptions. In the Information Age, the ever-increasing need for more data processing capabilities becomes inherent with the times, and with the addition of the Digital Age, it is assumed that increased data processing will continue to be conducted by discrete electronic computing systems and the many forms that they will take. The continued development of more efficient data models, and the database systems designed to leverage them, will become the chariot bringing forth the climax of the current times and the dawning of new endeavors for human curiosity and our willingness to learn and explore ever further into the beyond. Tackling these issues is the direct purpose of the LISA Universal Informationbase System (the LISA Informationbase), to effectively integrate data of diverse variations and in a semi-ubiquitous structure to increase data automation of information content for use by our patrons in a powerful database management technology. Surveyed in this chapter is a review of this driving technology and its applications, covering the NITA Methodology Stage-I, Stage-II, and Stage-III in its developmental process.

scholarly journals A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4831 ◽  
Author(s):  
Anup Vanarse ◽  
Adam Osseiran ◽  
Alexander Rassau ◽  
Peter van der Made
Keyword(s):  
Data Processing ◽  
Electronic Nose ◽  
Spike Timing ◽  
Sensor Data ◽  
Development Environment ◽  
Early Classification ◽  
Recent Developments ◽  
Efficient Data ◽  
Olfactory Systems

In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.

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