INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

By Wei Zhan, Liting Sun, Di Wang, Haojie Shi, Aubrey Clausse, Maximilian Naumann, Julius Kümmerle, Hendrik Königshof, Christoph Stiller, Arnaud de La Fortelle and 1 more. 2019

610 citations recorded by the source corpus.

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 this https URL.

Subfields: Artificial Intelligence, Automotive Engineering, Safety, Risk, Reliability and Quality

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Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “Recent work by authors [91] contains real world motions by various trafﬁc actors, observed in diverse interactive driving scenarios.”
The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections (2019, methodology): “In Table II, we compare the inD dataset with current public datasets, which are the most similar to the inD dataset: Crowds UCY/Zara [8], Ko-PER [18], DUT [20], CITR [20], Interaction [21], BIWI Hotel/ETH [10], VRU Trajectory”
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction (2021, background): “However, there is little consensus to date on the best modeling choices for each component, and in popular benchmark challenge datasets [6, 12, 18, 53], there is a surprisingly diverse set of solutions to this problem; for details see Section 2 and Table 2.”
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023, background): “Also influential are self-driving “motion prediction” datasets [11, 19, 29, 4, 45] — containing abstracted object tracks instead of raw sensor data — of which the Argoverse Motion Forecasting dataset [6] was the first.”
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset (2021, methodology): “∗ Interactions [38] gathered data from 4 countries including 6 cities (the last statistic is collected through personal communication) and the entire dataset is not divided into segments.”