A Scenario Distribution Model for Effective and Efficient Testing of 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.