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Real-time safety metrics are important for the automated driving system (ADS)to assess the risk of driving situations and to assist the decision-making.Although a number of real-time safety metrics have been proposed in theliterature, systematic performance evaluation of these safety metrics has beenlacking. As different behavioral assumptions are adopted in different safetymetrics, it is difficult to compare the safety metrics and evaluate theirperformance. To overcome this challenge, in this study, we propose anevaluation framework utilizing logged vehicle trajectory data, in that vehicletrajectories for both subject vehicle (SV) and background vehicles (BVs) areobtained and the prediction errors caused by behavioral assumptions can beeliminated. Specifically, we examine whether the SV is in a collisionunavoidable situation at each moment, given all near-future trajectories ofBVs. In this way, we level the ground for a fair comparison of different safetymetrics, as a good safety metric should always alarm in advance to thecollision unavoidable moment. When trajectory data from a large number of tripsare available, we can systematically evaluate and compare different metrics'statistical performance. In the case study, three representative real-timesafety metrics, including the time-to-collision (TTC), the PEGASUS CriticalityMetric (PCM), and the Model Predictive Instantaneous Safety Metric (MPrISM),are evaluated using a large-scale simulated trajectory dataset. The proposedevaluation framework is important for researchers, practitioners, andregulators to characterize different metrics, and to select appropriate metricsfor different applications. Moreover, by conducting failure analysis on momentswhen a safety metric failed, we can identify its potential weaknesses which arevaluable for its potential refinements and improvements.

Evaluation of automated driving system safety metrics with logged vehicle trajectory data