TY - GEN
T1 - Three-dimensional simulation for training autonomous vehicles in smart city environments
AU - Chu, Phuong Minh
AU - Wen, Mingyun
AU - Park, Jisun
AU - Kaisi, Huang
AU - Cho, Kyungeun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper proposes a photorealistic 3D city simulation method for training autonomous vehicles. The proposed method incorporates human simulation, animal simulation, vehicle simulation, and traffic light simulation. To generate natural actions for humans and animals, a motivation-based approach is first applied; then the Q-Network is used to select optimal goals depending on the motivations, and action plans are made based on a hierarchical task network. For vehicles, affinity propagation, data augmentation, and convolutional neural network are employed to generate driver driving data for realistic vehicle movement simulation. A traffic light system is also implemented based on rules derived from real-life observation. The results of experiments in which a virtual city was created demonstrate that the proposed method can simulate city environments naturally. The proposed method can be applied to various smart city applications, such as autonomous vehicle training systems.
AB - This paper proposes a photorealistic 3D city simulation method for training autonomous vehicles. The proposed method incorporates human simulation, animal simulation, vehicle simulation, and traffic light simulation. To generate natural actions for humans and animals, a motivation-based approach is first applied; then the Q-Network is used to select optimal goals depending on the motivations, and action plans are made based on a hierarchical task network. For vehicles, affinity propagation, data augmentation, and convolutional neural network are employed to generate driver driving data for realistic vehicle movement simulation. A traffic light system is also implemented based on rules derived from real-life observation. The results of experiments in which a virtual city was created demonstrate that the proposed method can simulate city environments naturally. The proposed method can be applied to various smart city applications, such as autonomous vehicle training systems.
KW - 3D simulation
KW - Autonomous vehicle
KW - Convolutional neural network
KW - Q-network
KW - Smart city
UR - http://www.scopus.com/inward/record.url?scp=85074851805&partnerID=8YFLogxK
U2 - 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00153
DO - 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00153
M3 - Conference contribution
AN - SCOPUS:85074851805
T3 - Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
SP - 848
EP - 853
BT - Proceedings - 2019 IEEE International Congress on Cybermatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
Y2 - 14 July 2019 through 17 July 2019
ER -