Experimental evaluation of Kalman predictive redundancy system for safety-critical systems

Kim, Man Ho; Park, Jee Hun; Lee, Suk; Lee, Kyung Chang

doi:10.1109/isie.2009.5222621

Cited by 1 publication

(1 citation statement)

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“…The analyser forecasts the distance values using Kalman prediction to determine whether the data from the radar sensor are erroneous. To forecast the k th distance d m ( k ) of the AHRS, the analyser estimates the state of the discrete-time controlled process which is governed by the linear stochastic difference equations 19,20…”

Section: Structure Of the Ahrsmentioning

confidence: 99%

Analytical hybrid redundancy system for the fault tolerance of advanced driver assistance systems

Lee

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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The growing dependence on electronic sensors has resulted in increased concerns over the fault tolerance in safety-critical systems. For example, a vehicle with an advanced driver assistance system, which assists safe driving using numerous electronic elements, should be fault tolerant because sensor failures can lead to events in which car occupants receive serious injuries. However, it is often undesirable to have multiple redundant sensors because of the high cost of critical sensors such as the radar in an advanced driver assistance system. To address this problem, we present an analytical hybrid redundancy system that provides fault tolerance using fault detection and exception-handling algorithms. On the basis of the actual range data from an advanced driver assistance system radar, we present the results of numerical simulations of the analytical hybrid redundancy system and show that it outperforms existing approaches with respect to a number of performance indices.

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Section: Structure Of the Ahrsmentioning

confidence: 99%