An Intelligent Driving Strategy Optimization Algorithm Assisted by Direct Acyclic Graph Blockchain and Deep Reinforcement Learning

HUANG Xiaoge; LI Chunlei; LI Wenjing; LIANG Chengchao; CHEN Qianbin

doi:10.11999/JEIT240407

Volume 46 Issue 12

Dec. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(12): 4363-4372

HUANG Xiaoge, LI Chunlei, LI Wenjing, LIANG Chengchao, CHEN Qianbin. An Intelligent Driving Strategy Optimization Algorithm Assisted by Direct Acyclic Graph Blockchain and Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4363-4372. doi: 10.11999/JEIT240407

Citation:

HUANG Xiaoge, LI Chunlei, LI Wenjing, LIANG Chengchao, CHEN Qianbin. An Intelligent Driving Strategy Optimization Algorithm Assisted by Direct Acyclic Graph Blockchain and Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4363-4372. doi: 10.11999/JEIT240407

Citation:

HUANG Xiaoge, LI Chunlei, LI Wenjing, LIANG Chengchao, CHEN Qianbin. An Intelligent Driving Strategy Optimization Algorithm Assisted by Direct Acyclic Graph Blockchain and Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4363-4372. doi: 10.11999/JEIT240407

PDF( 3509 KB)

An Intelligent Driving Strategy Optimization Algorithm Assisted by Direct Acyclic Graph Blockchain and Deep Reinforcement Learning

doi: 10.11999/JEIT240407 cstr: 32379.14.JEIT240407

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62371082, 62001076), Guangxi Science and Technology Project (AB24010317), The Natural Science Foundation of Chongqing (CSTB2023NSCQ-MSX0726, cstc2020jcyj-msxmX0878)

Received Date: 2024-05-25
Rev Recd Date: 2024-11-13

Available Online: 2024-11-19

Publish Date: 2024-12-01

Abstract

Abstract

The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic policy gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
- Intelligent driving,
- Data sharing,
- Deep Reinforcement Learning(DRL),
- Directed Acyclic Graph(DAG)

FullText(HTML)

References(15)

References

[1]	XU Wenchao, ZHOU Haibo, CHENG Nan, et al. Internet of vehicles in big data era[J]. IEEE/CAA Journal of Automatica Sinica, 2018, 5(1): 19–35. doi: 10.1109/JAS.2017.7510736.
[2]	TENG Siyu, HU Xuemin, DENG Peng, et al. Motion planning for autonomous driving: The state of the art and future perspectives[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(6): 3692–3711. doi: 10.1109/TIV.2023.3274536.
[3]	LI Guofa, QIU Yifan, YANG Yifan, et al. Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(3): 2197–2211. doi: 10.1109/TIV.2022.3227921.
[4]	ZHU Zhuangdi, LIN Kaixiang, JAIN A K, et al. Transfer learning in deep reinforcement learning: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11): 13344–13362. doi: 10.1109/TPAMI.2023.3292075.
[5]	WU Jingda, HUANG Zhiyu, HUANG Wenhui, et al. Prioritized experience-based reinforcement learning with human guidance for autonomous driving[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(1): 855–869. doi: 10.1109/TNNLS.2022.3177685.
[6]	CHEN Junlong, KANG Jiawen, XU Minrui, et al. Multiagent deep reinforcement learning for dynamic avatar migration in AIoT-Enabled vehicular metaverses with trajectory prediction[J]. IEEE Internet of Things Journal, 2024, 11(1): 70–83. doi: 10.1109/JIOT.2023.3296075.
[7]	ZOU Guangyuan, HE Ying, YU F R, et al. Multi-constraint deep reinforcement learning for smooth action control[C]. The 31st International Joint Conference on Artificial Intelligence, Vienna, Austria, 2022: 3802–3808. doi: 10.24963/ijcai.2022/528.
[8]	HUANG Xiaoge, WU Yuhang, LIANG Chengchao, et al. Distance-aware hierarchical federated learning in blockchain-enabled edge computing network[J]. IEEE Internet of Things Journal, 2023, 10(21): 19163–19176. doi: 10.1109/JIOT.2023.3279983.
[9]	CAO Bin, WANG Zixin, ZHANG Long, et al. Blockchain systems, technologies, and applications: A methodology perspective[J]. IEEE Communications Surveys & Tutorials, 2023, 25(1): 353–385. doi: 10.1109/COMST.2022.3204702.
[10]	HUANG Xiaoge, YIN Hongbo, CHEN Qianbin, et al. DAG-based swarm learning: A secure asynchronous learning framework for internet of vehicles[J]. Digital Communications and Networks, 2023. doi: 10.1016/j.dcan.2023.10.004.
[11]	XIA Le, SUN Yao, SWASH R, et al. Smart and secure CAV networks empowered by AI-enabled blockchain: The next frontier for intelligent safe driving assessment[J]. IEEE Network, 2022, 36(1): 197–204. doi: 10.1109/MNET.101.2100387.
[12]	FU Yuchuan, LI Changle, YU F R, et al. An autonomous lane-changing system with knowledge accumulation and transfer assisted by vehicular blockchain[J]. IEEE Internet of Things Journal, 2020, 7(11): 11123–11136. doi: 10.1109/JIOT.2020.2994975.
[13]	FAN Bo, DONG Yiwei, LI Tongfei, et al. Blockchain-FRL for vehicular lane changing: Toward traffic, data, and training safety[J]. IEEE Internet of Things Journal, 2023, 10(24): 22153–22164. doi: 10.1109/JIOT.2023.3303918.
[14]	YIN Hongbo, HUANG Xiaoge, WU Yuhang, et al. Multi-region asynchronous swarm learning for data sharing in large-scale internet of vehicles[J]. IEEE Communications Letters, 2023, 27(11): 2978–2982. doi: 10.1109/LCOMM.2023.3314662.
[15]	CAO Mingrui, ZHANG Long, and CAO Bin. Toward on-device federated learning: A direct acyclic graph-based blockchain approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(4): 2028–2042. doi: 10.1109/TNNLS.2021.3105810.