The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections

By Julian Bock, Robert Krajewski, Tobias Moers, Steffen Runde, L. Vater, Lutz Eckstein. 2020 IEEE Intelligent Vehicles Symposium (IV), 2019

487 citations recorded by the source corpus.

Automated vehicles rely heavily on data-driven methods, especially for complex urban environments. Large datasets of real world measurement data in the form of road user trajectories are crucial for several tasks like road user prediction models or scenario-based safety validation. So far, though, this demand is unmet as no public dataset of urban road user trajectories is available in an appropriate size, quality and variety. By contrast, the highway drone dataset (highD) has recently shown that drones are an efficient method for acquiring naturalistic road user trajectories. Compared to driving studies or ground-level infrastructure sensors, one major advantage of using a drone is the possibility to record naturalistic behavior, as road users do not notice measurements taking place. Due to the ideal viewing angle, an entire intersection scenario can be measured with significantly less occlusion than with sensors at ground level. Therefore, we created a comprehensive, large-scale urban intersection dataset with naturalistic road user behavior using camera-equipped drones as successor of the highD dataset. The resulting dataset contains more than 13 500 road users including vehicles, bicyclists and pedestrians at intersections in Germany and is called inD. The dataset consists of 10 hours of measurement data from four intersections and is available online for non-commercial research at: https://www.inD-dataset.com

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

View Paper: https://www.semanticscholar.org/paper/15ef0cb38239a1f74bea8ff9521778cad99420f3

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In Automotive Engineering

1. Karttikeya Mangalam
2. Jitendra Malik
3. Harshayu Girase
4. Yang An

Citations in the corpus

• From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting (2020, methodology): “Further, we also study Y -net’s performance in the proposed long term prediction setting on the Stanford Drone & the Inter-section Drone Dataset [5] where it substantially improves the performance of state-of-the-art short term methods by over 50 .”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “Drones HighD [90], inD [23] 110,000 Traffic prediction”