Abstract：
Crash scenario-based testing is crucial for assessing autonomous driving safety. However, existing studies on scenario generation tend to prioritize concrete scenarios for direct testing, neglecting the construction of fundamentally functional scenarios with a broader range. Police-reported historical crash data is a valuable supplement, yet detecting all potential crash scenarios is laborious. In order to address this issue, this study proposes an adaptive search sampling framework based on deep generative model and surrogate model (SM) to extract master scenario samples from police-reported historical crash data. The framework starts with selecting representative samples from the full crash dataset as initial master scenario samples using various sampling techniques. Evaluation indexes are then constructed, and derived scenario samples are synthesized using the deep generative model. To enhance efficiency, an SM is established to replace the generative model\'s training and data generation process. Based on the SM, an adaptive search sampling method is developed, which iteratively adjusts the sampling strategy using the Similarity Score to achieve comprehensive sampling. Experimental results demonstrate the notable advantage of the adaptive search sampling method over other sampling methods. Furthermore, statistical analysis and visualization assessments confirm the effectiveness and accuracy of the proposed method.
摘要：
基于碰撞场景的测试对于评估自动驾驶安全性至关重要。然而,现有的场景生成研究倾向于优先考虑直接测试的具体场景，忽略了具有更广泛范围的基本功能场景的构建。警方报告的历史事故数据是一个有价值的补充，然而，检测所有潜在的崩溃情况是费力的。为了解决这个问题,本研究提出了一种基于深度生成模型和代理模型（SM）的自适应搜索采样框架，以从警方报告的历史崩溃数据中提取主场景样本。该框架从使用各种采样技术从完整的崩溃数据集中选择代表性样本作为初始主场景样本开始。然后构建评价指标，和派生的场景样本使用深度生成模型进行合成。为了提高效率，建立SM来代替生成模型的训练和数据生成过程。基于SM，开发了一种自适应搜索采样方法，使用相似度评分对采样策略进行迭代调整，实现全面采样。实验结果表明，自适应搜索采样方法比其他采样方法具有显着的优势。此外，统计分析和可视化评估证实了所提出方法的有效性和准确性。
