Identifying and evaluating the safety performance of road vehicle automation is one of the major challenges still to solve before market introduction of these functions. The assessment of these functions is highly challenging due to the increasing complexity of algorithms for environment perception and decision-making. In addition, the achievable level of safety is still an open question. To evaluate the achievable level of safety, this thesis introduces a new data-driven framework for safety impact assessment of road vehicle automation. The presented framework features human driver performance as a reference for assessment. The developed framework comprises a three-step approach: First, the effectiveness fields, the accidents and situations potentially addressed by the automated driving functions, are identified and clustered in the input data into driving scenarios. The considered driving scenarios are derived from the accident type catalogue built on decades of experience in accident research and thus covering all physically possible accident constellations. It is assumed that these accident constellations do not change with road vehicle automation, while their frequency of occurrence and their severity may change. Second, the change of frequency of occurrence of driving scenarios can be determined for mixed traffic conditions using traffic simulations with novel driver behaviour models representing human errors in the traffic simulations. In a third step, the change in severity within driving scenarios is assessed using human driver performance models as a reference. Simulator studies showed that human driver performance is based on feedforward control in incident situations while reaction time and reaction intensity are gamma-distributed in the selected driver population. The distinction of the human driving behaviour in these two models is particularly sensible as driver behaviour models influence the number, hence the frequency of incident driving scenarios, while driver performance models reflect the behaviour within an incident, thus the severity of a driving scenario. In consequence, this partitioning reflects the structure of the overall assessment approach. Finally, the resulting assessment of changed frequency and severity within the classified scenarios can be scaled-up by using accident statistics. The novel framework is exemplarily applied to investigate the level of safety of two different automated driving functions operating in motorway- and urban domain. This proves that the developed approach is applicable to automated driving functions of all automation levels (SAE 1- 5) and operation domains.

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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.18154/rwth-2020-08950
Licence: CC BY-NC-SA license

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