Abstract
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.
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