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

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

[1]  Wenshuo Wang,et al.  Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios , 2020, IEEE Transactions on Intelligent Transportation Systems.

[2]  Klaus C. J. Dietmayer,et al.  The Ko-PER intersection laserscanner and video dataset , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[3]  Silvio Savarese,et al.  Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes , 2016, ECCV.

[4]  Martin Lauer,et al.  A Literature Review on the Prediction of Pedestrian Behavior in Urban Scenarios , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[5]  Stefan Becker,et al.  An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark , 2018, ArXiv.

[6]  Matthias Mayr,et al.  Lanelet2: A high-definition map framework for the future of automated driving , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[7]  Masayoshi Tomizuka,et al.  INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps , 2019, ArXiv.

[8]  Wei Chen,et al.  BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters , 2018, ArXiv.

[9]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Lutz Eckstein,et al.  The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[11]  Keith Redmill,et al.  Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[12]  Lutz Eckstein,et al.  A Traffic-based Method for Safety Impact Assessment of Road Vehicle Automation , 2018 .

[13]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[14]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[15]  Lutz Eckstein,et al.  VeGAN: Using GANs for Augmentation in Latent Space to Improve the Semantic Segmentation of Vehicles in Images From an Aerial Perspective , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[16]  Dani Lischinski,et al.  Crowds by Example , 2007, Comput. Graph. Forum.

[17]  Julian Bock,et al.  Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections , 2017, VEHITS.

[18]  Klaus C. J. Dietmayer,et al.  Intentions of Vulnerable Road Users—Detection and Forecasting by Means of Machine Learning , 2018, IEEE Transactions on Intelligent Transportation Systems.

[19]  Jalal Etesami,et al.  Causal Transfer for Imitation Learning and Decision Making under Sensor-shift , 2020, AAAI.

[20]  Weilong Song,et al.  Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection , 2016 .

[21]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.