Autonomous driving paper index
Engineering a cognition-based specification method
One-line summary
Abstract Context Software development is inherently a human cognitive task that involves the capture and integration of diverse knowledge and decisions from multiple stakeholders.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract Context Software development is inherently a human cognitive task that involves the capture and integration of diverse knowledge and decisions from multiple stakeholders. Existing specification methods and languages mostly rely on computer-based or mathematical primitives, leading to a disconnect between how people naturally think and communicate, and how systems are specified. Therefore, we are investigating a method, called MuDForM (Multi-Domain Formalization Method), to formalize and integrate the knowledge of multiple domains into domain models and into specifications in terms of those domain models. We created a first coherent definition of the method, which emerged from several case study evaluations published in previous works. Goal Establish a method definition that is explicitly based on concepts from human cognition. Method We studied literature in (language) philosophy, linguistics, and cognitive science, to identify concepts that can serve as the cognitive underpinning of the method’s metamodel. We made a model of those concepts and connected them to MuDForM’s metamodel via an explicit method definition structure and analyzed the result. Result The paper defines the conceptual and structural groundwork for illustrating how a specification method can be constructed based on insights into human cognition and communication. It provides a coherent model of cognitive specification aspects, which grounds the modeling concepts in MuDForM ’s metamodel and makes it cognition-based. We also identified additional cognitive aspects that call for future work and the possible extension of the metamodel. The paper clarifies MuDForM ’s objective to support the transformation of natural language into unambiguous, cognition-aligned models.
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