论文标题
自动驾驶的常识性视觉感官：关于广义神经主张在线绑架的整合视觉和语义
Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics
论文作者
论文摘要
我们证明了在自动驾驶的背景下，系统整合的视觉和语义解决方案的需求和潜力。使用答案集编程（ASP）的一般神经视觉方法用于在线视觉感官制作（ASP）是系统的正式化和完全实现的。该方法在视觉计算中集成了艺术状态，并作为模块化框架开发，通常在混合体系结构中用于实时感知和控制。我们与社区建立的基准Kittimod，MOT-2017和MOT-2020评估和演示。作为用例，我们关注以人为中心的视觉感官的意义（例如，涉及语义表示和解释性，提问，通用性，常识性插值）在安全至关重要的自主驾驶情况下。开发的神经束框架是独立于领域的，其自动驾驶案例旨在作为在某些以人为中心的AI技术设计考虑的背景下在各种认知互动环境中进行在线视觉感觉的典范。 关键词：认知愿景，深层语义，声明性的空间推理，知识表示和推理，常识性推理，视觉绑架，答案集编程，自动驾驶，以人为本的计算和设计，驾驶技术的标准化，空间认知和AI。
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. Keywords: Cognitive Vision, Deep Semantics, Declarative Spatial Reasoning, Knowledge Representation and Reasoning, Commonsense Reasoning, Visual Abduction, Answer Set Programming, Autonomous Driving, Human-Centred Computing and Design, Standardisation in Driving Technology, Spatial Cognition and AI.
