Enhancing Safety and Efficiency in Automated Container Terminals: Route Planning for Hazardous Material AGV Using LSTM Neural Network and Deep Q-Network-Reference-Cited by-同舟云学术

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Enhancing Safety and Efficiency in Automated Container Terminals: Route Planning for Hazardous Material AGV Using LSTM Neural Network and Deep Q-Network

Published:2024-03 Issue:1 Volume:7 Page:64-77
ISSN:2399-9802
Container-title:Journal of Intelligent and Connected Vehicles
language:
Short-container-title:J. Int. Con. Veh.

Author:

Li Fei¹,Cheng Junchi²,Mao Zhiqi³,Wang Yuhao³,Feng Pingfa¹

Affiliation:

1. Tsinghua University,Department of Mechanical Engineering,Beijing,China,100084

2. Information Science and Technology College, Dalian Maritime University,Dalian,China,116000

3. School of Vehicle and Mobility, Tsinghua University,Beijing,China,100084

Funder

Tsinghua University

Publisher

Tsinghua University Press

Link

http://xplorestaging.ieee.org/ielx7/9944931/10520218/10520170.pdf?arnumber=10520170

Reference29 articles.

1. Hybrid cooperative intersection management for connected automated vehicles and pedestrians

2. Port Yard Storage Optimization

3. Container port drayage operations and management: Past and future

4. Human-like lane-changing trajectory planning algorithm for human-machine conflict mitigation

5. An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

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1. Exploring the Potential of Generative Adversarial Networks in Enhancing Urban Renewal Efficiency;Sustainability;2024-07-06

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