Brief paper: A framework and automotive application of collision avoidance decision making

Authors:
Jonas Jansson;Fredrik Gustafsson
Affiliations:
The Swedish National Road and Transport Research Institute, Linköping, Sweden;Department of Electrical Engineering, Linköping University, Linköping, Sweden
Venue:
Automatica (Journal of IFAC)
Year:
2008

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Abstract

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.