TY - GEN
T1 - Deep Reinforcement Learning-based Context-Aware Redundancy Mitigation for Vehicular Collective Perception Services
AU - Jung, Beopgwon
AU - Kim, Joonwoo
AU - Pack, Sangheon
N1 - Funding Information:
ACKNOWLEDGEMENT This research was supported by National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) (No. 2020R1A2C3006786).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Collective perception service (CPS) is one of the most fundamental services in intelligent transportation systems. Since it can incur significant overhead in exchanging perceived object containers (POCs), european telecommunications standards institute (ETSI) introduced several redundancy mitigation schemes; however, there are several limitations in application to the vehicular environment. In this paper, we propose a deep reinforcement learning (DRL)-based context-Aware redundancy mitigation (DRL-CARM) scheme where various vehicular contexts (i.e., location, speed, heading, and perception area) are employed for redundancy mitigation. To derive the optimal policy on redundancy mitigation, the DRL-CARM scheme employs a deep Q-network (DQN) with a reward function on the usefulness of POC. Evaluation results demonstrate that the DRL-CARM scheme can improve the average usefulness of POC by 254% and reduce the network load by 49.4%, compared with conventional redundancy mitigation schemes.
AB - Collective perception service (CPS) is one of the most fundamental services in intelligent transportation systems. Since it can incur significant overhead in exchanging perceived object containers (POCs), european telecommunications standards institute (ETSI) introduced several redundancy mitigation schemes; however, there are several limitations in application to the vehicular environment. In this paper, we propose a deep reinforcement learning (DRL)-based context-Aware redundancy mitigation (DRL-CARM) scheme where various vehicular contexts (i.e., location, speed, heading, and perception area) are employed for redundancy mitigation. To derive the optimal policy on redundancy mitigation, the DRL-CARM scheme employs a deep Q-network (DQN) with a reward function on the usefulness of POC. Evaluation results demonstrate that the DRL-CARM scheme can improve the average usefulness of POC by 254% and reduce the network load by 49.4%, compared with conventional redundancy mitigation schemes.
KW - Collective Perception Service
KW - Deep Reinforcement Learning
KW - ETSI Redundancy Mitigation Scheme
KW - Intelligent Transportation System
UR - http://www.scopus.com/inward/record.url?scp=85125652269&partnerID=8YFLogxK
U2 - 10.1109/ICOIN53446.2022.9687254
DO - 10.1109/ICOIN53446.2022.9687254
M3 - Conference contribution
AN - SCOPUS:85125652269
T3 - International Conference on Information Networking
SP - 276
EP - 279
BT - 36th International Conference on Information Networking, ICOIN 2022
PB - IEEE Computer Society
T2 - 36th International Conference on Information Networking, ICOIN 2022
Y2 - 12 January 2022 through 15 January 2022
ER -