Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments. ARTICLE HISTORY

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This study compares real-traffic deceleration and cut-in scenarios, which were established as critical to automated vehicles (AVs) safety, between Japanese and German highway trajectory datasets. Both scenarios were extracted from two different traffic data previously collected in Japan with both instrumented vehicles and fixed cameras over highways (SAKURA dataset) and in Germany with drones (highD dataset). Five vehicle kinematic variables (lateral and longitudinal distances, velocities, and accelerations) were used to parameterize both scenarios and compared them between datasets using correlation and intersection objective measures and safety metrics: Time-to-Collision and Time Headway. Despite the differences in the rule of the road (e.g., speed limits, left-and right-hand traffic), road design, and data sources between the two countries, data comparison results revealed significant correlations and intersections of parameters distribution for both scenarios. The Time-to-Collision significantly overlapped between countries for both scenarios. However, differences in the Time Headway indicate that the safety distance varied across both countries, suggesting that safety assessment methodologies need to be tailored to different environments and regions to ensure safety. These results highlight the potential to develop safety indicators applicable at the international level and warrant further data collection and comparative studies that support the development of harmonized, widely applicable, and region-neutral AVs safety assessment methodologies.

Automated driving is currently under research in various projects and research activities. These activities are clustered in different research areas. The evaluation and sign-off methodology for automated vehicles is currently a challenge to be solved. An innovative approach for such a sign-off methodology is proposed. A state-of-the-art overview of existing evaluation methods for advanced driver assistance systems, which are already available on the market today, is given in the form of a systems engineering process model. The existing evaluation methods already require great effort for real-world testing. Applying these methods to automated vehicles will exceed reasonable budget and time. The proposed method for effective evaluation of automated vehicles considers a database of relevant driving situations, which are collected and simulated on the basis of accident data and field operational and dedicated studies under controlled conditions. The database of relevant driving situations is the basis for an effective evaluation to be applied in simulation, driving simulators, and in test track scenarios. The methodology is described, and necessary building blocks are provided to implement the proposed concept.