Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling
In this paper authors provide a means of decision making in terms of threat-assessment algorithms for road scenes where a number of objects are known and threat assessments can be provided given the objects’ current position and velocity. Future driver input variables that control the objects in the scene are an unknown and so, in order to properly model realistic driver behaviors, dynamic modeling is implemented as a probabilistic a priori and so computes the likelihood of a given driver input behavior. Future behaviors in the presented system are approximated using the Monte Carlo sampling method. Different types of road vehicle safety threat assessments like times to collisions or probabilities of collisions can be computed using the given Monte Carlo decision-making scheme. Some techniques are also presented to reduce the computational workload that is generally demanded by implementing the Monte Carlo method.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Authors:
- Eidehall, Andreas
- Petersson, Lars
- Publication Date: 2008-3
Language
- English
Media Info
- Media Type: Print
- Features: Figures; Photos; References; Tables;
- Pagination: pp 137-147
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 9
- Issue Number: 1
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Algorithms; Decision making; Field of vision; Highway safety; Monte Carlo method; Probability; Traffic distribution
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory;
Filing Info
- Accession Number: 01091132
- Record Type: Publication
- Source Agency: UC Berkeley Transportation Library
- Files: BTRIS, TRIS
- Created Date: Mar 31 2008 8:05AM