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Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2004 (English)Report (Other academic)
Abstract [en]

A growing research topic within the automotive industry is active safety systems. These systems aim at helping the driver avoid or mitigate the consequences of an accident. In this paper a collision mitigation system that performs late braking is discussed. The brake decision is based on estimates from tracking sensors. We use a Bayesian approach, implementing an extended Kalman filter (EKF) and a particle filter to solve the tracking problem. The two filters are compared for different sensor noise distributions in a Monte Carlo simulation study. In particular a bi-modal Gaussian distribution is proposed to model measurement noise for normal driving. For ideal test conditions the noise probability density is derived from experimental data. The brake decision is based on a statistical hypothesis test, where collision risk is measured in terms of required acceleration to avoid collision. The particle filter method handles this test easily. Since the test is not analytically solvable a stochastic integration is performed for the EKF method. Both systems perform well in the simulation study under the assumed sensor accuracy. The particle filter based algorithm is also implemented in a real-time testbed and fulfilled the on-line requirements.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. , 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2599
Keyword [en]
Bayes methods, Gaussian distribution, Kalman filters, Monte Carlo methods, Automobile industry, braking, Collision avoidance, Decision making, Road safety, Safety systems, Statistical testing, Stochastic processes, Tracking
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55982ISRN: LiTH-ISY-R-2599OAI: oai:DiVA.org:liu-55982DiVA: diva2:316752
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-13Bibliographically approved

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Karlsson, RickardJansson, JonasGustafsson, Fredrik

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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