## 3 Crash Prediction Testing Methodology
### Test Scenario Requirements  
A data-driven approach to scenario generation is chosen. Logical scenarios are utilized to describe the parameter ranges of the state values used for scenario representation and concrete test scenarios for the verification and validation (V&V) tests [20]. In order to describe driving scenarios, a five layer model is proposed by [PERSON] et al. [23]. The scenario is described by five levels: road (L1), traffic infrastructure (L2), temporary manipulation of L1 and L2 (L3), Objects (L4) and Environment (L5). [PERSON] et al. have proposed a six-layer model by adding digital information as a sixth layer and placing static objects on L2 [24]. To create concrete test scenarios with sufficient data depth for the perception-based crash prediction function, information from level one to five is required. Considering the increasing use of V2X communication in modern vehicles, L6 information is not currently part of this study, but should be used in future testing.  
Test scenarios for the CSP function must represent real-world driving situations and are divided into three scenario groups:  
* Collision
* Critical situation
* Regular driving  
For collision scenarios, all collisions in the area of action of the function should be considered. A critical situation extends from potential conflicts to near accident scenarios. Various criticality metrics are discussed by [PERSON], [PERSON] and [PERSON] [25]. The most common metrics with a focus on collision avoidance are time to collision (TTC), time to distance (TTD), time to brake (TTB) and time to steer (TTS). More advanced metrics, such as crash severity and collision configuration distribution, assess the critical situation risk based on the potential accident severity. To validate the Pre-Crash system, various critical situations from a low to high criticality level are required. Regular driving is the test baseline, it represents the different states in the area of action, such as different environmental, road, traffic infrastructure, traffic flow, lighting and other conditions.  
Considering the addressed Pre-Crash systems, concrete scenarios are classified by must fire, may fire and no fire scenarios. Exclusively for PCS classified as QM, the condition may fire is established. Considering the crash prediction function, must fire and may fire collisions, as well as may fire nearby collisions, are required to test TP (true positive) prediction. No fire collisions, nearby collisions and regular driving are required to test TN (true negative) crash prediction. Figure 4 shows different test scenario classifications and true prediction classes according to the system under test and their activation requirements. Any prediction assignments other than those shown in Figure 4 lead to false positive (FP) or false negative (FN) results.  
This results in different requirements for the information that is contained within the different scenario groups. All groups require dynamic and static information about the road network, road users and environment to build the test scenario, as defined for L1 to  
Figure 4: Test Scenario classification for Pre-Crash systems and must fire, may fire and no fire signals.  
L5 [23]. In addition, for collision scenarios, In-Crash information is required. For the testing of ASIL A+ Pre-Crash systems, it is necessary to define the ground truth of the technical crash severity, such as the crash pulse or delta-v values. For QM systems, the ground truth of at least a collision configuration is also sufficient.