Critical Factor Graph Situation Clusters for Accelerated Automotive Safety Validation

Modern validation approaches of advanced automotive safety systems involve simulations of human driving behavior in safety-critical traffic events. Critical situations are often painstakingly enumerated and modeled, and it is difficult to establish confidence that the space of critical traffic events is adequately covered. This work presents an automated method for identifying and clustering critical situations that capture severity and frequency of occurrence, thereby allowing for risk-based safety validation. We demonstrate the ability of the new approach to accelerate the safety validation of an automotive safety system using importance sampling and efficiently optimize its parameters.