3 edition of A safety-based decision making architecture for autonomous systems found in the catalog.
A safety-based decision making architecture for autonomous systems
Joseph C. Musto
1991 by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va .
Written in English
|Other titles||Safety based decision making architecture for autonomous systems.|
|Statement||by Joseph C. Musto and L.K. Lauderbaugh.|
|Series||NASA contractor report -- NASA CR-191864., CIRSSE report -- #98.|
|Contributions||Lauderbaugh, L. K., United States. National Aeronautics and Space Administration.|
|The Physical Object|
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Get this from a library. A safety-based decision making architecture for autonomous systems. [Joseph C Musto; L K Lauderbaugh; United States.
National Aeronautics and Space Administration.]. A tactical-level lane-driving and decision-making model that considers multisource information in a complex and dynamic urban environment is critical for the development of autonomous vehicles.
The decision-making architecture of an autonomous s ystem consists of three levels with decreasing decisional a utonomy [6 ]: the h igher “s trategic” level manage s goals.
convey the message autonomous vehicles are not science fiction anymore and these systems can be implemented on normal cars. A good example to a project Logic Processing Unit Sensors Mechanical Control Systems -laser sensors -cameras -radars -ultrasonic sensors -GPS, etc.
-Software -Decision making -Checking functionality -User interfaceFile Size: 1MB.  Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving.
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safety-based control are combined to avoid collisions. It was found that combination of DRL and safety-based control performs well in most scenarios.
In order to enable DRL to escape local optima, speed up the training process and avoid danger conditions or accidents, Survival-Oriented Reinforce-ment Learning (SORL) model is proposed in [ Multi-UAV Operations are an area of great interest in government, industry, and research community.
In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue or disaster relief.
As multi-UAV systems operate in an open operational environment, many disrupting events can occur. To this end, resilience of these systems is of Cited by: 1. John X.
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In reinforcement learning (RL) [Sutton and Barto], an agent learns to behave in an unknown environment based on the rewards it single objective RL these rewards are scalar. However, most real-life problems are more naturally expressed with multiple objectives.
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Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs.
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The characteristics of the steady-state flow for this model are Cited by: The specific topics discussed include requirements engineering for embedded software systems, tools and methods used in the automotive industry, software product lines, architectural frameworks, various related ISO standards, functional safety and safety cases, cooperative intelligent transportation systems, autonomous vehicles, and security.
The research on “AI”-based self-adaptation decision making is far from mature, an enormous technical challenges still have to be overcome [19,], especially the safety and correctness verification problem. As SCPS is safety-critical, it is important to guarantee the C&D of self-adaptation : Peng Zhou, Decheng Zuo, Kun Mean Hou, Zhan Zhang, Jian Dong, Jian-Jin Li, Haiying Zhou.
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Model-Driven Engineering (MDE) refers to a system development methodology where Cited by: Abstract. Proceedings of the Fourth International Conference on Transportation Engineering, held in Chengdu, China, OctoberSponsored by Southwest Jiaotong University; China Communications and Transportation Association; the Transportation & Development Institute of ASCE; Mao Yisheng Science and Technology Education Foundation; and Zhan Tianyou Development.
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Scientific Research Publishing is an academic publisher with more than open access journal in the areas of science, technology and medicine. It also publishes. The book caters to a diverse audience; anyone who uses analytical techniques in decision making will need this book. Of the many books available on this subject, most are not broad enough, not.
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A comparative analysis on decision-making certainty between the classical AHP and TOPSIS approach were also discussed.
Azadeh et al  presented a robust decision-making methodology based on Fuzzy Analytical Hierarchy Process (FAHP) for evaluating and selecting the appropriate software package. Business Modeling and Software Design 8th International Symposium, BMSD Vienna, Austria, July 2–4, Proceedings Editor Boris Shishkov Bulgarian Academy of Sciences, Institute of Mathematics and Informatics (IMI)/ Interdisciplinary Institute for Collaboration and Research on Enterprise Systems and Technology (IICREST) Soﬁa.
ly, making for faster and improved decision-making. Rethinking Industrial Safety In a Connected Enterprise, compa-nies can do more than improve how they monitor and manage safety — they can create innately safer opera-tions that complement production.
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