TY - GEN
T1 - Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving
AU - Hsu, Pei Yun
AU - Huang, Mei Lin
AU - Wang, Wei Yen
AU - Chiang, Hsin Han
N1 - Funding Information:
Supported in part by a grant from Varian Medical System (B.D.S.), National Institutes of Health, National Cancer Institute Grant No. R01 CA207216-01 (B.D.S., Y.-C.T.S., and S.H.G.), and The University of Texas MD Anderson Cancer Center under the Cancer Center Support Core Grant (CA16672). B.D.S. is supported by the Cancer Prevention & Research Institute of Texas (RP160674) and is an Andrew Sabin Family Fellow. S.H.G. is supported by Komen Grants No. SAC 150061 and CPRIT RP160674.
Funding Information:
Collection and assembly of data: Jennifer K. Logan, Benjamin D. Smith Data analysis and interpretation: Jennifer K. Logan, Jing Jiang, Ya-Chen Tina Shih, Xiudong Lei, Ying Xu, Karen E. Hoffman, Sharon H. Giordano, Benjamin D. Smith Manuscript writing: All authors Final approval of manuscript: All authors Accountable for all aspects of the work: All authors ACKNOWLEDGMENT Supported in part by a grant from Varian Medical System (B.D.S.), National Institutes of Health, National Cancer Institute Grant No. R01 CA207216-01 (B.D.S., Y.-C.T.S., and S.H.G.), and The University of Texas MD Anderson Cancer Center under the Cancer Center Support Core Grant (CA16672). B.D.S. is supported by the Cancer Prevention & Research Institute of Texas (RP160674) and is an Andrew Sabin Family Fellow. S.H.G. is supported by Komen Grants No. SAC 150061 and CPRIT RP160674.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The trajectory prediction of traffic agents plays an important role regarding to the safety of autonomous driving. Structured by gate recurrent unit (GRU), this paper proposes a new predict model with the combination of trajectory mapping method. The experimental results show that the proposed model can feasibly predict the future trajectories of the surrounding traffic agents in a mixed flow including vehicles, cyclists, and pedestrians.
AB - The trajectory prediction of traffic agents plays an important role regarding to the safety of autonomous driving. Structured by gate recurrent unit (GRU), this paper proposes a new predict model with the combination of trajectory mapping method. The experimental results show that the proposed model can feasibly predict the future trajectories of the surrounding traffic agents in a mixed flow including vehicles, cyclists, and pedestrians.
UR - http://www.scopus.com/inward/record.url?scp=85123048452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123048452&partnerID=8YFLogxK
U2 - 10.1109/ICCE-TW52618.2021.9603003
DO - 10.1109/ICCE-TW52618.2021.9603003
M3 - Conference contribution
AN - SCOPUS:85123048452
T3 - 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
BT - 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Y2 - 15 September 2021 through 17 September 2021
ER -