Abstract Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial real-world testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical. Thus, critical ones have to be prioritized. We have developed an approach for critical test scenario identification and in this study, we implement the approach and validate it on two real autonomous driving systems from industry by integrating it into their tool-chain. Our main contribution in this work is the demonstration and validation of our approach for critical scenario identification for testing real autonomous driving systems.

While autonomous driving systems are expected to change future means of mobility and reduce road accidents, understanding intensive and complex traffic situations is essential to enable testing of such systems under realistic traffic conditions. Particularly, we need to cover more relevant driving scenarios in the test. However, we do not want to spend time and resources testing useless scenarios that never happen in the real road traffic. In this work, we propose a new model that defines the distribution of scenarios using TTC (Time-to-Collision) for the vehicle–pedestrian interactions at unsignalized crossings based on the traffic density. The scenario distribution can be used as an input for test scenario generation and selection. We validate the model using real traffic data collected in Sweden and the result indicates that the model is effective and consistently upholds the real distribution, especially for critical scenarios with TTC less than 3 seconds. We also demonstrate the use of the model by connecting it to the testing of an auto-braking function from the industry. As a first step, our contribution is a model that predicts the worst-case distribution of scenarios using TTC and provides a mandatory input for testing autonomous driving systems.