The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Open accessAbstract
Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.com.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
Cited by
A Survey on Trajectory-Prediction Methods for Autonomous Driving
Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., & Chen, H. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3167103 · Cited by 677
Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., Yuan, J., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02067 · Cited by 577
Zhan, W., Sun, L., Wang, D., Shi, H., Clausse, A., Naumann, M., Kümmerle, J., Königshof, H., Stiller, C., Fortelle, A. D. L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03088 · Cited by 328
A Survey on Trajectory Data Management, Analytics, and Learning
Wang, S., Bao, Z., Culpepper, J. S., & Cong, G. (2022) · ACM Computing Surveys · doi:10.1145/3440207 · Cited by 267
Metadata
- Authors
- Krajewski, R., Bock, J., Kloeker, L., & Eckstein, L.
- Venue
- 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 2118-2125
- OpenAlex
- W2896642734
- Cited by
- 1,401
- References
- 4
- FWCI
- 75.36
- Type
- conference paper
- Language
- en
- Open access
- green
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- Krajewski, R., Bock, J., Kloeker, L., & Eckstein, L. (2018). The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). https://doi.org/10.1109/itsc.2018.8569552
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