Feasibility Evaluation of Oversize Load Transportation Using Conditional Rewarded Deep Q-Networks

It is crucial to determine feasibility when transporting oversize loads such as bridges or modular plants. In the past, swept path analysis was used to analyze the trajectory of the vehicle's movement. It is, however, a very time-consuming process. Additionally, these analysis tools do not supp...

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Bibliographic Details
Published inIEEE transactions on intelligent transportation systems Vol. 25; no. 6; pp. 5011 - 5021
Main Authors Son, Hojoon, Kim, Jongkyu, Jung, Hyunjin, Lee, Minsu, Lee, Soo-Hong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Automation
Bridges
Classifiers
conditional reward function
deep Q-networks
Feasibility studies
Image segmentation
Labeling
Labelling
Labels
Modular equipment
omnidirectional vehicle
oversize load
Roads
Shape
Trajectory
Trajectory analysis
Transportation
Transportation feasibility evaluation
vehicle simulation
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Summary:It is crucial to determine feasibility when transporting oversize loads such as bridges or modular plants. In the past, swept path analysis was used to analyze the trajectory of the vehicle's movement. It is, however, a very time-consuming process. Additionally, these analysis tools do not support omnidirectional vehicles, such as the self-propelled modular transporters, which transport oversize loads. The purpose of this research is to develop a simple simulator for an omnidirectional body and a simulation-based automated feasibility evaluation system to address these problems. The DQN agent moves the vehicle and then labels the training data of the binary classifier. DQN is trained quickly and effectively using curriculum learning and a conditional reward function. Through these auto-generated labels, a binary classifier can be trained with an AUC up to 0.9606. DQN agent-based automated labeling sometimes compensates for human manual labeling errors, which is one of the most compelling findings. Furthermore, binary classifiers are about 1000 times faster than conventional swept path analysis methods. This study introduces a system for determining the transportation feasibility of oversize loads efficiently and quickly.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3339143