发表机构
McMaster Centre for Software Certification (McSCert); McMaster University（麦克马斯特软件认证中心（McSCert）; 麦克马斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对自动驾驶 ego 中心数据集，提出结合场景图与 LTL 的场景提取方法，经 Argoverse 2 数据集验证，可有效提取并查询驾驶场景。
AI 中文摘要
从未标注的真实世界传感器数据流中提取场景是自动驾驶系统（ADS）开发过程中一项关键但具有挑战性的任务。自动筛选大型数据集以在空间和时间上定位关键场景，可实现对 ADS 数据集的基于场景的覆盖分析。本文提出一种使用场景图和线性时序逻辑（LTL）从 ego 中心数据集提取场景的方法：首先处理 ego 中心传感器数据和高清（HD）地图，生成代表驾驶场景的场景图序列；接着使用 LTL 正式指定感兴趣的驾驶场景，再使用现成的模型检查器，通过对照场景图序列评估 LTL 公式，从数据集中提取所有场景实例。该方法可用于模拟和真实世界数据集，我们在 Argoverse 2 的训练集和验证集（含 850 条 15 秒真实世界驾驶日志及若干行车记录仪视频）上对方法进行评估，通过对照基于轨迹标注和 HD 地图的规则基准进行测试，验证了所提方法在场景提取与查询方面的有效性。
英文摘要
Extracting scenarios from unlabelled real-world sensor data streams is a critical but challenging task in the development process of automated driving systems (ADS). Automatically sifting through large datasets to spatially and temporally locate critical scenarios can enable scenario-based coverage analysis of ADS datasets. In this paper, we present a method for extracting scenarios from egocentric datasets using scene graphs and Linear Temporal Logic (LTL). We first process egocentric sensor data and HD maps to generate a sequence of scene graphs representing a driving scenario. Next, we use LTL to formally specify driving scenarios of interest, then extract all instances of the scenarios from the dataset using an off-the-shelf model checker, which evaluates the LTL formula against the sequence of scene graphs. Our approach can be used on both simulated and real world datasets. We evaluate the method on the training and validation datasets from Argoverse 2 consisting of 850 15-second real-world driving logs, and several videos of dashcam footage. We demonstrate the effectiveness of our approach for extracting and querying scenarios by evaluating against a rule-based benchmark based on track annotations and HD maps.
