[论文解读] INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

Wei Zhan, Liting Sun+9 more|arXiv (Cornell University)|Sep 30, 2019

Autonomous Vehicle Technology and Safety参考文献 43被引用 354

一句话总结

本论文介绍 INTERACTION 数据集，这是一个由无人机和摄像头记录的、国际化、高度互动的驾驶运动数据集，带有语义HD地图，面向运动预测、规划、模仿学习和行为分析。

ABSTRACT

Behavior-related research areas such as motion prediction/planning, representation/imitation learning, behavior modeling/generation, and algorithm testing, require support from high-quality motion datasets containing interactive driving scenarios with different driving cultures. In this paper, we present an INTERnational, Adversarial and Cooperative moTION dataset (INTERACTION dataset) in interactive driving scenarios with semantic maps. Five features of the dataset are highlighted. 1) The interactive driving scenarios are diverse, including urban/highway/ramp merging and lane changes, roundabouts with yield/stop signs, signalized intersections, intersections with one/two/all-way stops, etc. 2) Motion data from different countries and different continents are collected so that driving preferences and styles in different cultures are naturally included. 3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants. Highly complex behavior such as negotiations, aggressive/irrational decisions and traffic rule violations are densely contained in the dataset, while regular behavior can also be found from cautious car-following, stop, left/right/U-turn to rational lane-change and cycling and pedestrian crossing, etc. 4) The levels of criticality span wide, from regular safe operations to dangerous, near-collision maneuvers. Real collision, although relatively slight, is also included. 5) Maps with complete semantic information are provided with physical layers, reference lines, lanelet connections and traffic rules. The data is recorded from drones and traffic cameras. Statistics of the dataset in terms of number of entities and interaction density are also provided, along with some utilization examples in a variety of behavior-related research areas. The dataset can be downloaded via https://interaction-dataset.com.

研究动机与目标

• 提供一个大规模、国际来源的互动驾驶场景数据集。
• 捕捉多样、复杂且关键的互动，包括对抗与合作行为。
• 包含完整的语义高清地图（lanelets、规则、参照）和完整的互动实体。
• 使在运动预测、模仿学习、决策制定、规划和社会行为生成等方面的研究成为可能。

提出的方法

• 从多国和大洲的无人机和交通摄像头收集互动驾驶数据。
• 使用稳定化、检测（Faster R-CNN）、数据关联、跟踪（Kalman）和平滑（RTS）对轨迹进行准确的边界框标注和地面平面轨迹。
• 构建厘米级高精度的高清 lanelet2 地图，具有物理和语义层（车道单元 lanelets、规则、参照）。
• 提供多样化的场景，包括环岛、无信号和有信号的交叉口、合流与变道。
• 使用如最小冲突点差异时间等指标来评估互动密度，并通过等待期来识别互动对。

实验结果

研究问题

• RQ1 在高度互动的驾驶场景中，国际背景下的驾驶行为有何差异？
• RQ2 在具有语义地图的高密度互动轨迹下，是否能改善预测、规划与模仿学习模型？
• RQ3 多样化场景中关键/互动事件（接近碰撞、激进操作）的特征与分布是什么？
• RQ4 完整的互动实体和地图可用性如何影响建模与规划的性能？

主要发现

• 数据集包括跨越多大洲的环岛、坡道、无信号和有信号交叉口等多样化场景。
• 它捕捉到高度互动且复杂的行为，包括对抗和合作运动，存在接近碰撞和轻微碰撞事件的实例。
• 提供具有完整语义信息的高清地图，能够进行语义感知的预测与规划。
• 互动密度指标显示 INTERACTION 的互动强度高于先前数据集如 highD 和 NGSIM，特别是在短 TTCP 差值(<1 s) 时。
• 数据支持用于运动预测、模仿学习、决策与规划验证，以及互动提取和社会行为生成。