WOD-E2E：面向挑战性长尾场景端到端驾驶的Waymo开放数据集
WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios
为视觉端到端驾驶提供了聚焦长尾场景的基准与基于人类偏好评分的开放环指标 RFS，可用于评估轨迹预测在罕见场景下的多模态合理性，替代传统距离指标。
原文依据 we propose a novel open-loop evaluation metric: Rater Feedback Score (RFS)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于视觉的端到端（E2E）驾驶因其可扩展性以及与多模态大语言模型（MLLM）的协同效应而受到研究界的关注。然而，当前的端到端驾驶基准主要包含常规场景，并搭配现有的开放环评测指标，这些指标在捕捉驾驶的多模态特性或有效评估长尾场景下的性能方面存在不足。为弥补这些空白，我们提出了面向端到端驾驶的Waymo开放数据集（WOD-E2E）。WOD-E2E包含4,021个驾驶片段（约12小时），专门针对日常生活中罕见、出现频率低于0.03%的挑战性长尾场景进行筛选。WOD-E2E中的每个片段均包含高层路径信息、自车状态以及来自8个环视相机的360度相机视图。为评估这些长尾情况下的端到端驾驶性能，我们提出了一种新颖的开放环评测指标：评分者反馈分数（RFS）。与传统基于距离的指标不同，RFS衡量预测轨迹与评分者标注的轨迹偏好标签的匹配程度。WOD-E2E包含用于验证的评分者偏好标签，并另设一个独立的留出测试集用于2025年WOD-E2E挑战赛基准排行榜。
英文原文摘要 1287 字符
Vision-based end-to-end (E2E) driving has garnered interest in the research community due to its scalability and synergy with multimodal large language models (MLLMs). However, current E2E driving benchmarks primarily feature nominal scenarios paired with existing open-loop evaluation metrics that fall short in capturing the multimodal nature of driving or effectively evaluating performance in long-tail scenarios. To address these gaps, we introduce the Waymo Open Dataset for End-to-End Driving (WOD-E2E). WOD-E2E contains 4,021 driving segments (approximately 12 hours), specifically curated for challenging long-tail scenarios that are rare in daily life with an occurring frequency of less than 0.03%. Each segment in WOD-E2E includes the high-level routing information, ego states, and 360-degree camera views from 8 surrounding cameras. To evaluate E2E driving performance on these long-tail situations, we propose a novel open-loop evaluation metric: Rater Feedback Score (RFS). Unlike conventional distance-based metrics, RFS measures how closely a predicted trajectory matches rater-annotated trajectory preference labels. WOD-E2E includes rater preference labels for validation, and a separate held out test set is used for the 2025 WOD-E2E Challenge benchmark leaderboard.