TY - GEN
T1 - SIND
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
AU - Xu, Yanchao
AU - Shao, Wenbo
AU - Li, Jun
AU - Yang, Kai
AU - Wang, Weida
AU - Huang, Hua
AU - Lv, Chen
AU - Wang, Hong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to the complexity and stochasticity, essential applications (e.g., behavior modeling, motion prediction, safety validation, etc.) at intersections rely heavily on data-driven techniques. Thus, there is an intense demand for trajectory datasets of traffic participants (TPs) in intersections. Currently, most intersections in urban areas are equipped with traffic lights. However, there is not yet a large-scale, high-quality, publicly available trajectory dataset for signalized intersections. Therefore, in this paper, a typical two-phase signalized intersection is selected in Tianjin, China. Besides, a pipeline is designed to construct a Signalized INtersection Dataset (SIND), which contains 7 hours of recording including over 13,000 TPs with 7 types. Then, the behaviors of traffic light violations in SIND are recorded. Furthermore, the SIND is also compared with other similar works. The features of the SIND can be summarized as follows: 1) SIND provides more comprehensive information, including traffic light states, motion parameters, High Definition (HD) map, etc. 2) The category of TPs is diverse and characteristic, where the proportion of vulnerable road users (VRU s) is up to 62.6% 3) Multiple traffic light violations of non-motor vehicles are shown. We believe that SIND would be an effective supplement to existing datasets and can promote related research on autonomous driving. The dataset is available online via: https://github.com/SOTIF-AVLab/SinD
AB - Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to the complexity and stochasticity, essential applications (e.g., behavior modeling, motion prediction, safety validation, etc.) at intersections rely heavily on data-driven techniques. Thus, there is an intense demand for trajectory datasets of traffic participants (TPs) in intersections. Currently, most intersections in urban areas are equipped with traffic lights. However, there is not yet a large-scale, high-quality, publicly available trajectory dataset for signalized intersections. Therefore, in this paper, a typical two-phase signalized intersection is selected in Tianjin, China. Besides, a pipeline is designed to construct a Signalized INtersection Dataset (SIND), which contains 7 hours of recording including over 13,000 TPs with 7 types. Then, the behaviors of traffic light violations in SIND are recorded. Furthermore, the SIND is also compared with other similar works. The features of the SIND can be summarized as follows: 1) SIND provides more comprehensive information, including traffic light states, motion parameters, High Definition (HD) map, etc. 2) The category of TPs is diverse and characteristic, where the proportion of vulnerable road users (VRU s) is up to 62.6% 3) Multiple traffic light violations of non-motor vehicles are shown. We believe that SIND would be an effective supplement to existing datasets and can promote related research on autonomous driving. The dataset is available online via: https://github.com/SOTIF-AVLab/SinD
UR - http://www.scopus.com/inward/record.url?scp=85141833587&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9921959
DO - 10.1109/ITSC55140.2022.9921959
M3 - Conference contribution
AN - SCOPUS:85141833587
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2471
EP - 2478
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2022 through 12 October 2022
ER -