The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Krajewski, Robert; Bock, Julian; Kloeker, Laurent; Eckstein, Lutz

doi:10.1109/itsc.2018.8569552

Cited by 898 publications

(475 citation statements)

References 15 publications

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“…Except for traffic monitoring, UAV-RS can also be used for traffic emergency monitoring and document, pedestrian-vehicle crash analysis and pedestrian/vehicle behavior study. In [186], the cameraequipped UAVs are used to record road traffic data, measure every vehicles position and movements from an aerial perspective for analyzing naturalistic vehicle trajectory and naturalistic driving behavior.…”

Section: Urban Traffic Monitoringmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

163

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The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

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Section: Urban Traffic Monitoringmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

163

View full text Add to dashboard Cite

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“…For example, in Stanford Drone dataset [11], the utilization of drone eliminated occlusion so that all participants (pedestrians, cyclists, cars, carts, buses) were individually tracked. Another dataset HighD [18], which focuses on vehiclevehicle interaction on highway driving, also successfully demonstrated the benefit of using the hovering drone to remove occlusion.…”

Section: Related Workmentioning

confidence: 93%

Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus

Yang

Redmill

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehiclecrowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion under the influence of nearby pedestrians and the vehicle. This study proposed two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. The trajectories of pedestrians, as well as vehicles, were extracted by processing video frames that come from a down-facing camera mounted on a hovering drone as the recording equipment. The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated. The statistics of the velocity magnitude distribution demonstrated the validity of the proposed dataset. In total, there are approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian trajectories in DUT dataset. The dataset is available at GitHub 1 .

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“…We conduct our experiment on two different datasets: The NGSIM I-80 dataset [21] and the HighD dataset [22]. 1) NGSIM: The NGSIM project's I-80 dataset contains trajectory data for vehicles in a highway merge scenario for three 15-minute timespans.…”

Section: A Datasetsmentioning

confidence: 99%

“…2) HighD: Since the NGSIM dataset still contains many artifacts (errors in bounding boxes, undetected cars, complete non-overlap of bounding box and true vehicle), we additionally conduct experiments on the new HighD dataset [22], which is a series of drone recordings and extracted vehicle features from about 400 meters each from several locations on the German Autobahn. A total of 16.5 h of data is available, containing 110 000 vehicles with a total driving distance of 45 000 km.…”

Section: A Datasetsmentioning

confidence: 99%

Graph Neural Networks for Modelling Traffic Participant Interaction

Diehl

Brunner

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

116

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By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract representation which allows us to apply Graph Neural Network (GNN) models for traffic prediction. These naturally take interaction between traffic participants into account while being computationally efficient and providing large model capacity. We evaluate two state-of-the art GNN architectures and introduce several adaptations for our specific scenario. We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account. This suggests that interaction is important, and shows that we can model it using graphs. This makes GNNs a worthwhile addition to traffic prediction systems. †

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The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Cited by 898 publications

References 15 publications

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus

Graph Neural Networks for Modelling Traffic Participant Interaction

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