Abstract:

Automated driving systems operate in complex and diverse environments. Considering the performance limitations of the sensors and the functional insufficiency of the perception algorithms under certain trigger conditions， it is inevitable that the upstream perception results of the autonomous driving system will be incorrect. Therefore， it is essential to test the robustness of decision-making and planning systems under conditions of erroneous upstream data to ensure the safety of automated driving. Firstly， in this paper， a data model based on a six-layer scenario ontology model and a fault model containing four types of uncertainty error patterns are proposed. Further， a generic fault injection framework named SOFIF is constructed to enable modification of upstream data. Finally， the robustness of two decision-making and planning systems under the error patterns of uncertainty existence is compared and analyzed based on Hardware-in-the-Loop （HiL） simulation testing， with the hazard rate proposed as the quantitative evaluation index. The hazard rate of the two tested systems is 0.89 and 0.64， respectively， indicating a large gap in the robustness of the two tested systems and further proving the effectiveness of SOFIF.

Key words: automated driving, safety of the intended functionality, testing and evaluation, robustness testing, fault injection