构建自动驾驶罕见场景模拟框架，提升系统安全性测试能力
3CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Driving
- 按状态、行为、证据三类构建罕见场景分类体系
- 实现32个可调参数的场景，支持9种天气与交通密度配置
- 为自动驾驶控制评估提供可重复、可修改的测试方案
我们提出基于CARLA模拟器的自动驾驶罕见场景仿真框架（3CSim），旨在弥补传统自动驾驶模型训练的局限性。该框架聚焦非标准、罕见且认知挑战性强的极端场景，这些场景对保障车辆安全与可靠性至关重要。我们建立了一套包含状态异常、行为异常和证据异常三类的罕见场景分类体系，并实现了32个可调节参数的独立场景，涵盖9种预设天气条件、时间设置及交通密度。该框架支持可重复、可修改的场景评估，有助于构建全面的数据集以供后续分析。
原文摘要 · Abstract (English)
We present the CARLA corner case simulation (3CSim) for evaluating autonomous driving (AD) systems within the CARLA simulator. This framework is designed to address the limitations of traditional AD model training by focusing on non-standard, rare, and cognitively challenging scenarios. These corner cases are crucial for ensuring vehicle safety and reliability, as they test advanced control capabilities under unusual conditions. Our approach introduces a taxonomy of corner cases categorized into state anomalies, behavior anomalies, and evidence-based anomalies. We implement 32 unique corner cases with adjustable parameters, including 9 predefined weather conditions, timing, and traffic density. The framework enables repeatable and modifiable scenario evaluations, facilitating the creation of a comprehensive dataset for further analysis.
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