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The Language of Driving (LoD) describes the implicit and explicit signals that allow autonomous vehicles and humans to understand each other in mixed traffic [1–3].
Driving behavior can be analyzed as a layered communication system:
An autonomous vehicle must infer human intent and simultaneously display legible intent of its own [2].
Driving “languages” vary globally; hence interfaces must maintain universal meaning while allowing local adaptation [1]. Behavior should be recognizable but not anthropomorphic, preserving clarity across cultures [3].
Field experiments using light-based cues have shown that simple color and motion patterns effectively communicate awareness and yielding. Participants reported improved understanding when signals were consistent and redundant across modalities [2].
Formalizing LoD as a measurable framework is essential for verification, standardization, and interoperability of automated behavior [3].
References: [1–3]