SINADRA: Towards a Framework for Assurable Situation-Aware Dynamic Risk Assessment of Autonomous Vehicles

Abstract

Assuring an adequate level of safety is the key challenge for the approval of autonomous vehicles (AV). The full performance potential of AV cannot be exploited at present because traditional assurance methods at design time are based on a risk assessment involving worst-case assumptions about the operating environment. Dynamic Risk Assessment (DRA) is a novel technique that shifts this activity to runtime and enables the system itself to assess the risk of the current situation. However, existing DRA approaches neither consider environmental knowledge for risk assessments, as humans do, nor are they based on systematic design-time assurance methods. To overcome these issues, in this paper we introduce the model-based SINADRA framework for situation-aware dynamic risk assessment. It aims at the systematic synthesis of probabilistic runtime risk monitors employing tactical situational knowledge to imitate human risk reasoning with uncertain knowledge. To that end, a Bayesian network synthesis and assurance process is outlined for DRA in different operational design domains and integrated into an adaptive safety management architecture. The SINADRA monitor intends to provide an information basis at runtime to optimally balance residual risk and driving performance, in particular in non-worst-case situations.