Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning

Heiberg, Amalie; Meyer, Eivind; Rasheed, Adil; San, Omer; Varagnolo, Damiano

doi:10.1016/j.neunet.2022.04.008

Cited by 23 publications

(19 citation statements)

References 29 publications

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“…rough a large number of studies, they believe that the quality of accounting information of an enterprise has a great relationship with the company shares held by the managers. e lower the shareholding ratio, the higher the quality of accounting information [6]. Lin and others have the same research results on accounting information and corporate governance.…”

Section: Literature Reviewmentioning

confidence: 67%

Financial Accounting Information Data Analysis System Based on Internet of Things

2022

Mathematical Problems in Engineering

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In order to meet the intelligent demand of modern financial data analysis, this paper proposes a financial accounting information data analysis system based on the Internet of things. Based on the central reinforcement learning architecture, the model uses multiple execution modules to enhance the computing and generalization ability of the single-agent reinforcement learning algorithm. In the selection of reinforcement learning algorithm, the instantaneous time difference algorithm is introduced. The algorithm can synchronize the experience of the previous iteration state in the learning process and does not depend on the final prediction value, which greatly saves the storage cost. In the establishment of the financial data analysis index system, the paper comprehensively considers the enterprise’s operation, development, debt repayment, and other capabilities, ensuring the integrity and rationality of the index system. In order to evaluate the performance of the algorithm, this paper takes the real financial data as the sample and uses BP neural network to conduct a comparative experiment. The experimental results show that the recognition accuracy of the model is better than that of the BP neural network in each experimental scenario, and the recognition accuracy of Experiment 3 is improved by 4.6%. Conclusion. The performance of the distributed reinforcement learning algorithm is better than that of the common back-propagation neural network in the real data set scenario.

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Section: Literature Reviewmentioning

confidence: 67%

Financial Accounting Information Data Analysis System Based on Internet of Things

2022

Mathematical Problems in Engineering

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“…The most relevant maritime traffic rules for collision avoidance are specified in the COLREGS [16]. Often, these traffic rules are indirectly integrated in the motion planning approach, e.g., through geometric thresholds [17]- [22], virtual obstacles [23], or cost functions [5], [24]- [28]. However, these approaches usually do not capture the temporal properties of collision avoidance rules, and the implemented interpretation of the COLREGS is often intransparent.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…A well-suited machine learning approach to solve motion planning tasks in uncertain environments is RL [5], [6], [34], [35]. The regarded scenario is usually on the open sea with other non-reactive dynamic obstacles [6], [35] and static obstacles [5], [34]. To achieve a behavior that complies with maritime traffic rules, the reward function considers rule compliance to minimize risks, but does not guarantee compliance because the reward function is only maximized.…”

Section: Related Workmentioning

confidence: 99%

“…Reinforcement Learning (RL) has provided promising results for a variety of motion planning tasks, e.g., autonomous driving [1], [2], robotic manipulation [3], [4], and autonomous vessel navigation [5], [6]. RL algorithms require random exploration to learn a capable policy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Safe Reinforcement Learning for Urban Driving using Invariably Safe Braking Sets

Krasowski

Zhang

Althoff

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Autonomous vehicles have to obey traffic rules. These rules are often formalized using temporal logic, resulting in constraints that are hard to solve using optimization-based motion planners. Reinforcement Learning (RL) is a promising method to find motion plans adhering to temporal logic specifications. However, vanilla RL algorithms are based on random exploration, which is inherently unsafe. To address this issue, we propose a provably safe RL approach that always complies with traffic rules. As a specific application area, we consider vessels on the open sea, which must adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). We introduce an efficient verification approach that determines the compliance of actions with respect to the COLREGS formalized using temporal logic. Our action verification is integrated into the RL process so that the agent only selects verified actions. In contrast to agents that only integrate the traffic rule information in the reward function, our provably safe agent always complies with the formalized rules in critical maritime traffic situations and, thus, never causes a collision.

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Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

Chhotu,

Suman

2024

Urban Rail Transit

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With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods.

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Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning

Cited by 23 publications

References 29 publications

Financial Accounting Information Data Analysis System Based on Internet of Things

Financial Accounting Information Data Analysis System Based on Internet of Things

Safe Reinforcement Learning for Urban Driving using Invariably Safe Braking Sets

Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

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