Driven by Commonsense

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, nonmonotonic 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.

Authors

• Jakob Suchan
• Mehul Bhatt
• Srikrishna Varadarajan

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