Collection and classification of influence parameters for safety effectiveness of ADAS - Guo, Fengwei; Fuchs, Anton; Kirschbichler, Stefan; Sinz, Wolfgang; Tomasch, Ernst; Steffan, Hermann; Moser, Joerg (Frontiers in Future Transportation, 2023)
Virtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of Advanced Driver Assistance Systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critical scenarios to improve the efficiency of testing without compromising credibility. One popular method is to explore the parameterized scenario space using various intelligent search methods. Selecting parameters to parameterize the scenario space is particularly important to achieve good coverage and high efficiency. However, an extensive collection of (relevant) influence parameters is missing, which allows a thorough consideration when selecting parameters regarding specific scenarios. In addition, the general importance definition for the individual influence parameters is not provided regarding the potential influence of their variations on the safety effectiveness of ADAS, which can also be used as a reference while selecting parameters. Combining knowledge from different sources (Published literature, standardized test scenarios, accident analysis, autonomous vehicle disengagement, accident reports and specific online surveys) this paper has summarized in total 94 influence parameters and given general definitions of importance for 77 influence parameters based on cluster analysis algorithms. The list of influence parameters provides researchers and system developers with a comprehensive basis for pre-selecting influence parameters for evaluating the safety effectiveness of ADAS using virtual scenario-based testing and helps to check whether certain influence parameters can be a meaningful extension for the evaluation.