We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking in the backdrop of autonomous driving. A general neurosymbolic method for online visual sensemaking using answer set programming (ASP) is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework that is generally usable within hybrid architectures for realtime perception and control. We evaluate and demonstrate with community established benchmarks KITTIMOD, MOT-2017, and MOT-2020. As use-case, we focus on the significance of human-centred visual sensemaking —e.g., involving semantic representation and explainability, question-answering, commonsense interpolation— in safety-critical autonomous driving situations.

The developed neurosymbolic framework is domain-independent, with the case of autonomous driving designed to serve as an exemplar for online visual sensemaking in diverse cognitive interaction settings in the backdrop of select human-centred AI technology design considerations.

我们展示了在自动驾驶背景下系统集成视觉和语义解决方案用于视觉感知的需求和潜力。在线的一般神经符号方法使用答案集编程 (ASP) 的视觉意义构建被系统地形式化并完全实施。该方法集成了视觉计算中的最新技术，并被开发为模块化框架，通常可在混合架构中用于实时感知和控制。我们使用社区建立的基准 KITTIMOD、MOT-2017 和 MOT-2020 进行评估和演示。作为用例，我们关注以人为中心的视觉意义构建——例如，涉及语义表示和可解释性、问答、常识插值——在安全关键的自动驾驶情况下。

开发的神经符号框架是独立于领域的，自动驾驶的案例旨在作为选择以人为中心的人工智能技术设计考虑背景下的不同认知交互环境中的在线视觉感知的范例。