Corner Test Cases for ADAS and HAVs: A Computational Study on the Influence of Road Irregularities on Vehicle Vision Systems

Automated Vehicles and next generation ADAS hold the promise of disrupting mobility. However, public field trials have recently highlighted road anomalies, such as potholes and bumps, as a source of autopilot disengagements. In this paper, we research the influence of road anomalies on the performance of Artificial Intelligence-based vision systems. To this end, we conducted controlled real-world experiments and developed a validated vehicle system computational model using IPG Carmaker. The vehicle detection, tracking and distance estimation performance have been investigated by undertaking a thorough sensitivity analysis. The results indicate the system limitations in performing adequately for a range of bump sizes and vehicle speeds. With our findings we put emphasis on the importance of vehicle dynamics in the development of automated driving systems.