Human-like route planning for automatic collision avoidance using generative adversarial imitation learning

Higaki, Takefumi; Hashimoto, Hirotada

doi:10.1016/j.apor.2023.103620

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…Song et al [33] proposed an integrated classification model based on supervised learning for ship collision-avoidance course prediction, aiming to simulate the human collision-avoidance decision-making process to predict the CA steering direction of ship operators. Higaki et al [34] proposed a human-like automatic collision-avoidance path-planning method based on generative adversative imitation learning (GAIL). Applying GAIL to ship collision avoidance addresses the challenge of designing appropriate rewards in DRL and the limitations of inverse reinforcement learning (IRL) to generate collision-avoidance routes that mimic the performance of human experts.…”

Section: Introductionmentioning

confidence: 99%

A Construction Method of a Sequential Decision Chain for Unmanned-Ship Autonomous Collision Avoidance Based on Human-Like Thinking

Wang,

Wang

et al. 2023

JMSE

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As one of the key technologies restricting the development of intelligent ships, autonomous collision avoidance has attracted the attention of many scholars all over the world. Existing research on collision-avoidance behavior focuses more on collision risk assessment and local path-planning methods for studies on the human-like sequential logic of the whole collision-avoidance process, as well as the decision-making process of various stages. Further in-depth thinking is needed urgently. Based on this, a construction method of a human-like sequential decision chain for the autonomous collision avoidance of unmanned ships is proposed through the construction of a collision-avoidance rule base and strategy set, efficient data access based on the Knowledge Graph concept, global collision risk assessment considering sequential decision process, and the construction of a complete collision-avoidance logic process to simulate the decision-making process of humans in complex multi-ship encounters in open waters. For multi-ship encounter scenarios, considering the sequential decision-making process of collision avoidance, a method was proposed to divide the collision risk of the target ship into direct collision risk and potential collision risk. The validity and reliability of the constructed sequential decision chain are verified by simulation experimental results. The results show that the method is effective for collision avoidance (especially multi-ship collision avoidance) in open waters and can provide a theoretical basis and technical support with good interpretability for the decision-making process of an unmanned ship’s autonomous collision avoidance.

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Section: Introductionmentioning

confidence: 99%