Discovery of Perception Performance Limiting Triggering Conditions in Automated Driving

Abstract

Highly automated driving (HAD) vehicles are complex systems operating in an open context. Performance limitations originating from sensing and understanding the open context under triggering conditions may result in unsafe behavior, thus, need to be identified and modeled. This aspect of safety is also discussed in standardization activities such as ISO 21448, safety of the intended functionality (SOTIF). Although SOTIF provides a non-exhaustive list of scenario factors to identify and analyze performance limitations under triggering conditions, no concrete methodology is yet provided to identify novel triggering conditions.We propose a methodology to identify and model novel triggering conditions in a scene in order to assess SOTIF using Bayesian network (BN) and p-value hypothesis testing. The experts provide the initial BN structure while the conditional belief tables (CBTs) are learned using dataset. P-value hypothesis testing is used to identify the relevant subset of scenes. These scenes are then analyzed by experts who provide potential triggering conditions present in the scenes. The novel triggering conditions are modeled in the BN and retested. As a case study, we provide p-value hypothesis testing of BN of LIDAR using real world data.

Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems

“…Te ISO 21448 suggests the systematic identifcation of triggering conditions so as to reduce the set of unknown hazardous scenarios. Among the other methods [8][9][10], on a more abstract level, Neurohr et al [11] proposed a criticality analysis as an approach. It decomposes the OD according to infuencing factors that are associated with increased criticality, that is, the so-called criticality phenomena (CP).…”