Driven By Commonsense On The Role Of Human-Centred Visual Explainability For Autonomous Vehicles

Within the autonomous driving domain, there is now a clear need and tremendous potential for hybrid solutions (e.g., integrating semantics, learning, visual computing) towards fulfilling essential legal and ethical responsibilities involving explainability (e.g., for diagnosis), human-centred AI (e.g., interaction design), and industrial standardisation (e.g, pertaining to representation, realisation of rules & norms). In these contexts, this highlight paper positions recent research from IJCAI 2019 [4] aimed at advancing human-centred AI principles in the backdrop of the autonomous driving application domain. From a technical viewpoint, the highlighted research provides a model for advancing the state of the art in reasoning about space and motion, combining reasoning and learning, non-monotonic reasoning, and computational modelling of high-level visuospatial commonsense. In addition to demonstrating the significance of integrated vision and semantics solutions in autonomous driving, we also highlight open questions emphasising the need for interdisciplinary mixed-methods research-involving AI, Psychology, HCI- to better appreciate the complexity and spectrum of varied human-centred challenges in diverse naturalistic driving situations.