## 2 Materials and Methods  
### Pre-Crash Systems in Compliance with International Safety Standards  
The concept of perception-based Pre-Crash safety systems goes back at least two decades. A few examples of potential Pre-Crash systems are the seat belt pre-tensioner [5],  
Figure 1: Schematic representation of 5 phases of the traffic accident and addressed driver assistance and safety systems. the airbag preset [5], unavoidable collision mitigation through crash constellation optimization [6] and Pre-Crash seat adjustment in highly automated vehicles [7]. Pre-Crash sensing based on a radar sensor is described by [PERSON] [3]. The necessity to reproduce real world collisions to test a Pre-Crash prediction performance on representative scenarios was recognized by [5]. Due to safety risks and high costs, crash prediction cannot be tested on a sufficiently high number of real crash tests. Reference [5] presents a scenario-based Pre-Crash system validation approach in a fully virtual environment. The predicted parameters in this study are the collision unavoidability, relative collision velocity, impact zone and impact angle, but not the technical crash severity. A real time technical crash severity estimation based on predicted crash constellation was presented by [8]. The approach combines machine learning and a 2D mass-spring-damper model to predict the distribution of expected crash pulses after the crash. [PERSON], [PERSON] and [PERSON] show the benefit of a reduced occupant forward displacement for reversible Pre-Crash seat belt pre-pretensioning (PPT) in comparison with a conventional belt system [9]. The effects of automated emergency braking (AEB) in combination with PPT forces of 0, 300 and 600 N, in high severity frontal crashes, were examined by [10]. The rib fracture risk has been found to reduce in proportion to the PPT force increase. Nevertheless, even the non-invasive use of the crash severity prediction function can lead to an increase in road safety. A method of preventive identification of accident black spots using the CSP function and four approaches to road safety increase is presented by [PERSON] [11]. A holistic top-down approach in which occupant protection is paramount was presented by [12]. This was to be achieved through perception-based crash prediction and the optimization of restraint strategies. To test the effectiveness of innovative restraint strategies, relevant accident test cases were extracted from the GIDAS (German In-Depth Accident Study) database. Subsequently, finite element method (FEM) vehicle to vehicle collision simulations and vehicle occupant simulations, including triggering the restraint systems, are carried out with conventional and optimized restraint strategies, which require a predictive estimation of crash severity. By analyzing the injury values, the safety potential of optimized restraint strategies is demonstrated [12]. The question of a reliable validation concept for a crash severity prediction function still remains open. Safety relevant electrical and electronic (E/E) road vehicle systems are developed to meet international functional safety requirements which are defined by the ISO 2626 standard [13]. The standard considers systematic and random faults on system, hardware and software levels, to determine whether the system meets functional and technical safety requirements. As a part of this process, hazard analysis and risk assessment (HARA) is performed to identify potential hazards and risks in the operating conditions. Based on HARA, the system is classified with an automotive safety integrity level (ASIL). ASIL classification helps to specify the safety requirements of the system and identify the tolerable risk as well as the accepted probability of system failure [13]. Thus, ASIL can be understood as a risk classification, which is a function of the three factors: Severity of possible injuries (S), probability of Exposure (E) and Controllability (C).