A Framework and Automotive Application of Collision Avoidance Decision Making
2008 (English)In: Automatica, ISSN 0005-1098, Vol. 44, no 9, 2347-2351 p.Article in journal (Refereed) Published
Collision avoidance (CA) systems are applicable for most transportation systems ranging from autonomous robots and vehicles to aircraft, cars and ships. A probabilistic framework is presented for designing and analyzing existing CA algorithms proposed in literature, enabling on-line computation of the risk for faulty intervention and consequence of different actions. The approach is based on Monte Carlo techniques, where sampling-resampling methods are used to convert sensor readings with stochastic errors to a Bayesian risk. The concepts are evaluated using a real-time implementation of an automotive collision mitigation system, and results from one demonstrator vehicle are presented.
Place, publisher, year, edition, pages
Elsevier, 2008. Vol. 44, no 9, 2347-2351 p.
Automotive control, Decision support, Decision theory, Collision avoidance, Non-linear filtering, Kalman filter
IdentifiersURN: urn:nbn:se:liu:diva-45856DOI: 10.1016/j.automatica.2008.01.016OAI: oai:DiVA.org:liu-45856DiVA: diva2:266752
© 2008 Elsevier Ltd. All rights reserved.2009-10-112009-10-112013-07-22