Risk-informed collision avoidance system design for maritime autonomous surface ships

Lee, Paul; Theotokatos, Gerasimos; Boulougouris, Evangelos; Bolbot, Victor

doi:10.1016/j.oceaneng.2023.113750

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…Therefore, the coplanar, angular, and nominal distance constraints of the linear features on the calibration board are introduced, and the best-fit method is employed to suppress the interference of the measurement tails, which enables high-precision calibration of the LiDAR. Figure 3 The coordinates of the point cloud collected by the LiDAR are defined as Equation (1).…”

Section: Accuracy Evaluation Model Of Lidar Measurementsmentioning

confidence: 99%

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Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

Liang,

Zhao,

Wang

et al. 2023

JMSE

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As the key route detection device, the performance of marine LiDAR in harsh environments is of great importance. In this paper, a metric reliability analysis method for marine LiDAR systems under extreme wind loads is proposed. First, a static measurement accuracy evaluation model for the LiDAR system is proposed, targeting the problem that the LiDAR measurement tail reduces the measurement accuracy. Second, the distribution of extreme wind speeds in the Pacific Northwest is investigated, and a wind load probability model is developed. Finally, the impact of hull fluctuations on LiDAR measurement accuracy is analyzed by performing hull fluctuation simulations based on the wind load probability model, and the relationship curve between the metric reliability and measurement accuracy of marine LiDAR systems under extreme wind loads is addressed using the Monte-Carlo method. Experimental results show that the proposed LiDAR static measurement accuracy evaluation model can improve the measurement accuracy by more than 30%. Meanwhile, the solved curve of the LiDAR metric reliability versus the measurement allowable error indicates that the metric reliability can reach above 0.89 when the allowable error is 60 mm, which is instructive for the reliable measurement of marine LiDAR systems during ship navigation.

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Section: Accuracy Evaluation Model Of Lidar Measurementsmentioning

confidence: 99%

“…LiDAR measurements play a crucial role in navigation safety assurance. Therefore, the metric reliability of marine LiDAR systems is key to achieve accurate monitoring of route obstacles [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

Liang,

Zhao,

Wang

et al. 2023

JMSE

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“…However, despite the current advancements of sensor technologies and unique features of each perception sensor [20],degradation of measurements due to sensor noise is inevitable [21]. Sensor noise refers to the random variation inherent in the measurements that can be attributed to a wide range of factors from intrinsic limitations, such as a low resolution or design imperfections, to extrinsic influences, such as atmospheric conditions or electromagnetic interference [22].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Lee,

Theotokatos,

Boulougouris

2024

JMSE

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Autonomous ships are expected to extensively rely on perception sensors for situation awareness and safety during challenging operations, such as reactive collision avoidance. However, sensor noise is inevitable and its impact on end-to-end decision-making has not been addressed yet. This study aims to develop a methodology to enhance the robustness of decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels. A Gaussian-based noisy perception sensor is employed, where its noisy measurements and noise variance are incorporated into the decision-making as observations. A deep reinforcement learning agent is employed, which is trained in different noise variances. Robustness metrics that quantify the robustness of the agent’s decision-making are defined. A case study of a container ship using a LIDAR in a single static obstacle environment is investigated. Simulation results indicate sophisticated decision-making of the trained agent prioritising safety over efficiency when the noise variance is higher by conducting larger evasive manoeuvres. Sensitivity analysis indicates the criticality of the noise variance observation on the agent’s decision-making. Robustness is verified against noise variance up to 132% from its maximum trained value. Robustness is verified only up to 76% when the agent is trained without the noise variance observation with lack of its prior sophisticated decision-making. This study contributes towards the development of autonomous systems that can make safe and robust decisions under uncertainty.

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Improving safety of interactions between conventional and autonomous ships

Rødseth¹,

Wennersberg

Nordahl

2023

Ocean Engineering

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Risk-informed collision avoidance system design for maritime autonomous surface ships

Cited by 7 publications

References 36 publications

Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Improving safety of interactions between conventional and autonomous ships

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