Towards self-aware vehicle automation for improved usability and safer automation mediation
Gabriel Rodrigues De Campos (Zenseact)
Alessia Knauss (Zenseact)
Nikita Tanov (Zenseact)
David Mano (Zenseact)
Bram Bakker (Cygnify BV)
Haneen Farah (TU Delft - Civil Engineering & Geosciences)
Yufei Yuan (TU Delft - Civil Engineering & Geosciences)
Stefan Andersson (Autoliv Inc.)
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Abstract
This paper investigates the development of self-aware mechanisms for automated vehicles, introducing the notion of an automation state estimation system. This system is capable to understand its capabilities in a given context, and can leverage that knowledge to estimate the current and near-future automation performance based on internal metrics, as well as external, static (e.g. lane geometry) and dynamic environmental elements (e.g. traffic and weather information). From an application perspective, we consider automation state estimation in the scope of automation mediation, as part of a broader and holistic mediation system, with the goal to tackle challenging aspects related to transitions of control, mode confusion, and driver engagement. We used real-world data for system design, and implemented the proposed automation estimation system in a prototype vehicle. Based on 70 hours of real-world driving, we also validated the performance of the automation state estimation for automation mediation purposes.