Uncertainty propagation in criticality measures for driver assistance

Active safety systems employ surround environment perception in order to detect critical driving situations. Assessing the threat level, e.g. the risk of an imminent collision, is usually based on criticality measures which are calculated from the sensor measurements. However, these metrics are subject to uncertainty. Probabilistic modelling of the uncertainty allows for more informed decision making and the derivation of sensor requirements. This work derives closed-form expressions for probability distributions of criticality measures under both state estimation and prediction uncertainty. The analysis is founded on uncertainty propagation in non-linear motion models. Finding the distribution of model-based criticality metrics is then performed using closed-form expressions for the collision probability and error propagation in implicit functions. All results are illustrated and verified in Monte-Carlo simulations.

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