A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Zhou, Zhengyu; Zhang, Yingjun; Wang, Shaobo

doi:10.3390/jmse9111202

Cited by 23 publications

(11 citation statements)

References 43 publications

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“…Using MaxEnt IRL, we estimated reward function from the pseudo-expert data. We solved 22 encounter scenarios called Imazu problem [29], which had been widely used to validate ship collision avoidance systems [15,30,31]. Its schematic is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Investigation and Imitation of Human Captains’ Maneuver Using Inverse Reinforcement Learning

Higaki

Hashimoto

Yoshioka

2022

Preprint

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Automatic collision avoidance is of significant importance to prevent maritime collisions. Although many studies have been conducted in recent years, autonomous system has not completely replaced human captains since it is still difficult to imitate their complicated decisions. Thus, the present paper tries to investigate and imitate experienced captains’ maneuver using maximum entropy inverse reinforcement learning (MaxEnt IRL). We firstly verify that MaxEnt IRL can reproduce appropriate reward function from demonstrative trajectories. Afterwards, we conduct an experiment on a simulator where well-experienced captains maneuver in congested sea and estimate reward from the trajectories. Searching the route which maximizes the obtained reward, finally, we demonstrate the optimized route can avoid collision against multiple ships in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs).

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Section: Resultsmentioning

confidence: 99%

Investigation and Imitation of Human Captains’ Maneuver Using Inverse Reinforcement Learning

Higaki

Hashimoto

Yoshioka

2022

Preprint

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“…With the development of artificial intelligence technology, some artificial intelligence applications, such as intelligent operation and maintenance [1][2][3]5] and intelligent transportation [10], have attracted researchers' attention. Time series anomaly detection for KPIs is a research hotspot in the field of intelligent operation and maintenance.…”

Section: Kpi Time Series Anomaly Detection Methodsmentioning

confidence: 99%

Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM

Shang

Zhang

et al. 2021

Applied Sciences

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KPIs (Key Performance Indicators) in distributed systems may involve a variety of anomalies, which will lead to system failure and huge losses. Detecting KPI anomalies in the system is very important. This paper presents a time series anomaly detection method based on correlation analysis and HMM. Correlation analysis is used to obtain the correlation between abnormal KPIs in the system, thereby reducing the false alarm rate of anomaly detection. The HMM (Hidden Markov Model) is used for anomaly detection by finding the close relationship between abnormal KPIs. In our correlation analysis of abnormal KPIs, firstly, the time series prediction model (1D-CNN-TCN) is proposed. The residual sequence is obtained by calculating the residual between the predicted value and the actual value. The residual sequence can highlight the abnormal segment in each data point and improve the accuracy of anomaly screening. According to the obtained residual sequence, these abnormal KPIs are preliminarily screened out from the historical data. Next, KPI correlation analysis is performed, and the correlation score is obtained by adding a sliding window onto the obtained anomaly index residual sequence. The correlation analysis based on the residual sequence can eliminate the interference of the original data fluctuation itself. Then, a correlation matrix of abnormal KPIs is constructed using the obtained correlation scores. In anomaly detection, the constructed correlation matrix is processed to obtain the adaptive parameters of the HMM model, and the trained HMM is used to quickly discover the abnormal KPI that may cause a KPI anomaly. Experiments on public data sets show that the method obtains good results.

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“…Imazu [14] put forward 42 encounter situations called the "Imazu problem" to specifically study how to carry out a validation. Zhou and Ahmed [15,16] made use of the first set of 22 encounter situations to test their collision avoidance algorithms. Johansen [17] proposed 15 collision avoidance scenarios for ships, according to the number of target ships, the wind, and the currents at sea.…”

Section: Introductionmentioning

confidence: 99%

Randomly Testing an Autonomous Collision Avoidance System with Real-World Ship Encounter Scenario from AIS Data

Zhu

Zhou

2022

JMSE

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Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method.

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A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships

Cited by 23 publications

References 43 publications

Investigation and Imitation of Human Captains’ Maneuver Using Inverse Reinforcement Learning

Investigation and Imitation of Human Captains’ Maneuver Using Inverse Reinforcement Learning

Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM

Randomly Testing an Autonomous Collision Avoidance System with Real-World Ship Encounter Scenario from AIS Data

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