| Abstract |
| Autonomous vehicle (AV) accidents involve the system's automation level alongside perception, judgment, and control processes. Current traffic accident reports fail to capture this technical context because they focus on physical evidence like vehicle position and collision type. This study addresses this gap by developing an icon-based visualization system for AV accident reports grounded in a previously proposed Core Information Framework covering vehicle, environmental, and human factors. We designed 26 icons across 10 types by integrating ISO standards (3864 and 7010), Gestalt principles, established information design theories, and police reporting conventions. Each icon's form was optimized for a 1 cm print size to reflect practical medium constraints. Twenty experts across relevant fields evaluated the icons using ISO 9186-2 criteria (meaningfulness, legibility, and semantic appropriateness) alongside perceived trustworthiness for official reporting. All measures scored above the scale midpoint (overall means 3.69-3.76), indicating that the system functions as a readable visual language. In particular, the communication, traffic infrastructure, and AV icons received high ratings, and all four expert groups rated the system consistently above 3.6 on average. Ultimately, the proposed system offers a structured visual language for AV accident documentation and demonstrates how practical media conditions and perceptual demands shape public information visualization. |
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| Key Words |
| Autonomous Vehicle Accident, Accident Investigation Report, Information Visualization, Core Information Framework, Icon System, 자율주행차사고, 사고조사서, 정보시각화, 핵심정보프레임워크, 아이콘시스템 |
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