Performance validation of artificial intelligence-based vehicle vision systems and the role of vehicle vertical dynamics

Abstract

Automated Vehicles and next generation Advanced Driver Assistance Systems 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 dissertation, we research the influence of road irregularities 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.

Date of Award	Jan 2020
Original language	English
Awarding Institution	Coventry University
Sponsors	European Union Horizon 2020
Supervisor	Stratis Kanarachos (Supervisor)