TY - GEN
T1 - Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model
AU - Hsu, Pei Yun
AU - Lin Huang, Mei
AU - Chiang, Hsin Han
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/23
Y1 - 2020/10/23
N2 - A predicting model based on long-short-term-memory (LSTM) and gated recurrent unit (GRU) is proposed to assist autonomous vehicles (AVs) to drive safely. To understand the behaviors of surroundings under a mixed scene of vehicles, bicycles, and pedestrians, the proposed model can predict the future trajectory of each object with models constructed by GRU. Since different objects have diverse behaviors, this paper applies different models to different categories for vehicles, pedestrians, and cyclists. For each object, the proposed model considers three observed trajectories with different time steps as the input data to predict a more accurate future trajectory. The proposed model is verified and compared with LSTM and GRU on KITTI dataset in the conducted experiments.
AB - A predicting model based on long-short-term-memory (LSTM) and gated recurrent unit (GRU) is proposed to assist autonomous vehicles (AVs) to drive safely. To understand the behaviors of surroundings under a mixed scene of vehicles, bicycles, and pedestrians, the proposed model can predict the future trajectory of each object with models constructed by GRU. Since different objects have diverse behaviors, this paper applies different models to different categories for vehicles, pedestrians, and cyclists. For each object, the proposed model considers three observed trajectories with different time steps as the input data to predict a more accurate future trajectory. The proposed model is verified and compared with LSTM and GRU on KITTI dataset in the conducted experiments.
KW - GRU
KW - autonomous vehicles (AVs)
KW - deep learning
KW - trajectory prediction
UR - http://www.scopus.com/inward/record.url?scp=85099581062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099581062&partnerID=8YFLogxK
U2 - 10.1109/ECICE50847.2020.9301976
DO - 10.1109/ECICE50847.2020.9301976
M3 - Conference contribution
AN - SCOPUS:85099581062
T3 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
SP - 263
EP - 266
BT - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
Y2 - 23 October 2020 through 25 October 2020
ER -