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 - 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 -