Real World Observations, Maneuver Estimation and Behavioral Predictability
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3597-6_2
Reference21 articles.
1. Barmpounakis E, Nikolas G (2020) On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment. Trans Res Part C Emerg Technol 111:50–71. https://doi.org/10.1016/j.trc.2019.11.023
2. 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. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 2118–2125. https://doi.org/10.1109/ITSC.2018.8569552
3. Makridis M, Konstantinos M, Aikaterini, Biagio C (2021) OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems. Trans Res Part C Emerg Technol 125:103047. https://doi.org/10.1016/j.trc.2021.103047
4. U.S. Department Of Transportation Federal Highway Administration (2017) Next generation simulation (NGSIM) vehicle trajectories and supporting data. Not Available. https://data.transportation.gov/d/8ect-6jqj.
5. Ye XQ, Mao S, Li HY, Shi YF, Li YY, Wang GJ, Tan X, Errui D (2022) Rope3D: the roadside perception dataset for autonomous driving and monocular 3D object detection task. arXiv:2203.13608. arXiv. http://arxiv.org/abs/2203.13608
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