Assessing Autonomous Emergency Braking: A Robust Approach Using Phenomenological Sensor Models

Abstract

The assessment of driver assistance system effectiveness in crash scenarios can be conducted through simulations. This approach allows for system improvements and contributes to overall road safety. However, it is important to recognize that simulations are an approximation of reality. Specifically, simulation models can significantly impact the results. In our work, we focus on sensor models that provide environmental information to driver assistance systems. Real-world sensor data differ from idealized simulation data due to measurement errors and environmental influences. We evaluate various sensor models for their applicability in simulating effectiveness assessments. Our research is implemented using the open-source platform openPASS. We investigate the impact of inaccurate sensor data on the effectiveness of an autonomous emergency braking system based on longitudinal traffic crashes from the German In-Depth Accident Study database. To achieve this, we compare stochastic phenomenological models with ideal sensor models. In the evaluation of the modeled autonomous emergency braking, we observed a major influence on the effectiveness due to the less accurate, more realistic sensor data. This evaluation approach enables early optimization of the considered driver assistance system’s robustness against inaccuracies.