## 5 Conclusions  
In this paper, the authors have elaborated on the safety potentials and risks of innovative crash severity prediction and Pre-Crash systems. A novel validation strategy for the crash severity prediction function was proposed in the context of ISO 26262 and ISO/PAS 21448 standards. The relevance of a reliable and precise real-time crash severity prediction function was demonstrated using the potential Pre-Crash use cases. Thus, the authors identified fundamental differences in the validation requirements of QM- and ASIL A+-related CSP functions. Test scenario classification criteria for collisions, critical situations and regular driving, combined with must fire, may fire and no fire signals, were presented. In addressing a scenario-based testing approach, unsupervised machine learning was chosen as an exploratory data analysis technique to extract representative and relevant test scenarios from real-world data. A novel, unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs were developed with K-Means++ and k-NN algorithms. The methodology was implemented on the GIDAS database to create 35 clusters of representative collision configurations in the area of action of the CSP function under test. The presented methodology for machine learning scenario extraction is transferable to other databases and can be carried out for different systems under test. Furthermore, not only the fulfillment of the ASIL-classified probability of failure requirements, but additionally the evidence of a positive risk balance were defined as essential conditions for the proof of safety of Pre-Crash systems. However, the limitations of the presented method have been disclosed.  
In the future, the research should focus on the identified limitations. The difficulties in mining representative traffic data with corresponding data depth, such as crash severity information, should be solved as soon as possible. That being the case, fleet data appears as the most promising source for the representative traffic data collection. Moreover, the combination of In-Crash and Pre-Crash variables should be considered in further scenario extraction. One question that still remains unanswered is what injury reduction benefit the Pre-Crash systems can provide through a specific prediction performance. For this reason, methods to generate fire signals from crash prediction distributions must be developed in the future. The complete chain from crash severity prediction to the activation of Pre-Crash systems and passenger injury analysis should be taken into account for a valid effectiveness and safety assessment of the crash severity prediction function.  
Conceptualization, R.P., A.N. and R.L.; methodology, R.P.; investigation, R.P.; resources, A.L.; data curation, R.P.; writing--original draft preparation, R.P.; writing--review and editing, A.N., A.L. and R.L.; visualization, R.P.; supervision, A.N., A.L. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research project is supported by Volkswagen AG. The results, opinions and conclusions expressed in this publication are those of the authors and do not necessarily represent the views of Volkswagen AG.  
Not applicable.  
[PERSON], [PERSON] and [PERSON] possess a valid data use license for the GIDAS and GIDAS PCM datasets.  
The authors declare no conflict of interest.