The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today’s society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application’s requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle’s safety performance are a key requirement for demonstrating its trustworthiness.

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a wellestablished concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically chooseThe research leading to these results is funded by the German Federal Ministry for Economic Affairs and Energy within the projects 'VVM -Verification & Validation Methods for Automated Vehicles Level 4 and 5' and 'SET Level -Simulation-based Development and Testing of Automated Driving', based on a decision by the Parliament of the Federal Republic of Germany.

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The electrification of the automotive powertrain provides completely new control options regarding the distribution of individual wheel moments. The integration of up to four independently controlled electrical engines in a vehicle allows individual adjustment of driving and braking torques to the current driving situation. Thus, electrical engines create a new kind of dynamic vehicle control. Unlike the Electronic Stability Control (ESC), Torque-Vectoring influences the vehicle dynamics not only through braking forces but also by setting up positive driving torques allowing for a new way of dynamic driving. In this paper two different control algorithms are developed in order to calculate a desired yaw moment to influence vehicle dynamics. The Torque-Vectoring algorithm distributes the yaw moment among the four wheels. The evaluation of the vehicle dynamic simulation has shown that the best results regarding the control quality can be reached by using the Fuzzy control algorithm to optimize the driving stability in extreme driving situations.

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