Uncertainty Aware Data Driven Precautionary Safety for Automated Driving Systems Considering Perception Failures and Event Exposure

Ensuring safety is arguably one of the largest remaining challenges before wide-spread market adoption of Automated Driving Systems (ADSs). One central aspect is how to provide evidence for the fulfilment of the safety claims and, in particular, how to produce a predictive and reliable safety case considering both the absence and the presence of faults in the system. In order to provide such evidence, there is a need for describing and modelling the different elements of the ADS and its operational context: models of event exposure, sensing and perception models, as well as actuation and closed-loop behaviour representations. This paper explores how estimates from such statistical models can impact the performance and operation of an ADS and, in particular, how such models can be continuously improved by incorporating more field data retrieved during the operation of (previous versions of) the ADS. Focusing on the safe driving velocity, this results in the ability to update the driving policy so to maximise the allowed safe velocity, for which the safety claim still holds. For illustration purposes, an example considering statistical models of the exposure to an adverse event, as well as failures related to the system's perception system, is analysed. Estimations from these models, using statistical confidence limits, are used to derive a safe driving policy of the ADS. The results highlight the importance of leveraging field data in order to improve the system's abilities and performance, while remaining safe. The proposed methodology, leveraging a data-driven approach, also shows how the system's safety can be monitored and maintained, while allowing for incremental expansion and improvements of the ADS. QC 20220511SALIENCE4CAV (2020-02946)WAS