Jan Erik Stellet

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In this work, we propose a novel taxonomy to partition the problem of testing advanced driver assistance systems (ADAS) into three basic dimensions. These dimensions are detailed and confirmed with recent research. Our framework permits the consideration of open research questions which have to be answered to pave the way for future highly automated(More)
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(More)
Recognising the intended manoeuvres of other traffic participants is a crucial task for situation interpretation in driver assistance and autonomous driving. While many works propose algorithms for (computationally feasible) inference, much less attention is paid to finding analytic upper performance bounds for these problems. This work studies the(More)
In this contribution, we address the model-based derivation of perception requirements based on upper bounds on vehicle localization uncertainty for urban driver assistance (UDA) and urban automated driving (UAD). We show that a probabilistic model for the estimation of map-relative localization accuracy can be obtained and utilized for proper(More)
Vehicle motion models are employed in driver assistance systems for tracking and prediction tasks. For probabilistic decision making and uncertainty propagation, the prediction's inaccuracy is taken into account in the form of process noise. This work estimates Gaussian process noise models from measured vehicle trajectories using the expectation(More)
Landmark-based localization in dynamic environments poses high demands on the perception system of a mobile robot. The pose estimate generally has to fulfill specific accuracy requirements which might be necessitated by dependent systems, such as behavior planning. Thus, in this contribution we focus on the model-based derivation of perception requirements,(More)
This contribution investigates algorithms for egomotion estimation from environmental features. Various formulations for solving the underlying procrustes problem exist. It is analytically shown that in the 2-D case this can be performed more efficiently compared to common implementations based on matrix decompositions. Furthermore, analytic error(More)
Testing of advanced driver assistance systems demands a highly accurate representation of the vehicle's environment, e.g. obtained by laser scanner sensors. In contrast to typical on-line assistance functions, the purpose of generating reference data allows full batch processing of the raw sensor measurements. Therefore, object tracking algorithms can make(More)
Autonomous emergency brake (AEB) systems have to decide on brake interventions based on an uncertain and incomplete perception of the environment. This paper analyses theoretical limitations in AEB systems caused by noisy sensor measurements and uncertain prediction models. Such performance bounds can be used to derive sensor accuracy constraints, to(More)