Fabian Peng Kärrholm

To develop a self-driving vehicle, there’s a large need for real world scenarios to be used for validation and verification of the autonomous drive function. Real world scenarios need to be collected by vehicles driving in the intended operational domain of the function. This work focuses on evaluating the collected data in the form of time-series from those vehicles, and classifying it into different states, events and scenarios. The found items in the data can then be used to conduct simulations of the AD function, risk analysis of intended launch areas and traffic behavior assessment. The presentation will cover different methods to analyze and structure the data, and ways to draw conclusions about traffic behaviour based on it.

Fabian Peng Kärrholm has worked across a variety of fields and started his career in computational fluid dynamics and has a PhD in Combustion & Thermodynamics. He also worked with combustion engine control system for several years. For the past five years he has worked with data analysis at the department for Autonomous Drive at Volvo Cars. Currently, he’s working with scenario extraction, identification, and classification for traffic analysis.