The Impact of Autonomous Ships on Safety at Sea – A Statistical Analysis

Vos, Jiri de; Hekkenberg, Robert; Banda, Osiris A. Valdez

doi:10.1016/j.ress.2021.107558

Cited by 73 publications

(20 citation statements)

References 18 publications

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“…Due to their size, these ships have small damage and intact stability and can be easily capsized after collision with the MASS, endangering the lives of passengers/crew (Atzampos et al., 2018). This is in line with several studies highlighting the high accident rates on small ships (de Vos et al., 2021; Wang et al., 2021). The possibility of collisions with other ships or icebergs were considered as less severe (S = 4).…”

Section: Resultssupporting

confidence: 92%

A method to identify and rank objects and hazardous interactions affecting autonomous ships navigation

2022

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The Autonomous Navigation System (ANS) constitutes a critical key enabling technology required for operating Maritime Autonomous Surface Ships (MASS). To assure the safety of MASS operations, the effective identification of potential objects and target ships interacting with the own MASS is quintessential. This study proposes a systematic method to identify the items interacting with the own MASS. This method is based on a similar approach previously employed for the encountering items’ identification in robotics, which is customised herein for the MASS needs. The developed method is applied to a short-sea shipping MASS. The environmental features, agents and objects related to her navigation are identified and ranked based on the frequency of encounter and the potential collision consequences. The results demonstrate the ability of the method to identify additional items in comparison to Automatic Identification System based data. The interactions with the small ships are considered as the most critical, due to their potential accidental consequences and their exhibited high frequency of encounter. This study results are employed to support the ANS design and testing of the investigated ship.

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Section: Resultssupporting

confidence: 92%

A method to identify and rank objects and hazardous interactions affecting autonomous ships navigation

2022

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“…Therefore, based on the planned global path, the USV should also be able to perform real-time local obstacle avoidance to meet the requirements of successful and safe navigation [ 3 , 4 ]. Addiitonally, recent reports have indicated that many marine collision accidents are related to human decision failures concerning compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) [ 5 , 6 ]. Therefore, it is also necessary to include COLREGs as an integral element in the USV collision avoidance path planning system.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs

Rong

Tong

et al. 2022

Sensors

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Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework’s effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.

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“…In recent years, there has been an increased interest in MASS that is anticipated to improve the safety and efficiency of WTS, work environment for seafarers (Wariishi, 2019), and reduce the number of accidents (de Vos et al, 2021). In order to promote the development of MASS, more time should be calculated to contend with social and technical factors that have thus far limited MASS reaching its full potential.…”

Section: Introductionmentioning

confidence: 99%

Constructing Knowledge Maps for Situation Awareness of Maritime Autonomous Surface Ships

Song¹,

Papadimitriou²,

Negenborn³

et al. 2022

Preprint

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Many projects related to maritime autonomous surface ships (MASS) have been proceeding to date, which promotes the commercialization of MASS. It is anticipated that there will be ships with different degrees of autonomy coexisting in a waterborne transport system (WTS) in the near future, forming a mixed waterborne transport system (MWTS). To ensure navigational safety, the ship needs to be well aware of the situation in real time, i.e. be able to exhibit situation awareness. The inconsistency of comprehension within SA of the same situation could occur in the MWTS, leading to a potentially dangerous situation. As such, it is essential to unify the SA framework for MASS to eliminate the inconsistency with human operators. It is challenging but necessary for a MASS to accomplish the process of situation awareness involving perception, comprehension, and projection. Especially the part of comprehension is the core element that needs to be addressed well and enhanced further. One possible way to reach it is to integrate the information given by the perception layer, projection layer, as well as additional domain knowledge like navigational rules to conduct further analysis. Accordingly, the current paper proposes a method for knowledge integration of SA for the MASS. The method realizes four capabilities of SA to satisfy relevant requirements in maritime domain: a general map, risk assessment enrichment, temporal and dynamic features, as well as a supplement of domain knowledge. For that purpose, the paper takes two steps: (i) constructing a SA framework for MASS, in which the entities related to SA are classified to different categories. (ii) proposing an ontology-based SA comprehension model where the information of entities are integrated together and then the SA can be depicted for MASS in real time. A case study is provided to how the model can be applied in maritime domain, in which a MASS is approaching a port executing its tasks. As a result, the proposed method can relate the information provided by both the perception and projection layers, and domain knowledge in the form of a knowledge graph to depict the real-time situation. The results show that the method is feasible to provide potentials to the MASS to be aware of the situation in real time considering domain knowledge. The method can be applied to MASS for the information fusion of situation awareness, which also can be supplied to Maritime Safety and Security (MSS) organizations such as Maritime Safety Administration or Coast Guard for traffic surveillance of WTS.

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The Impact of Autonomous Ships on Safety at Sea – A Statistical Analysis

Cited by 73 publications

References 18 publications

A method to identify and rank objects and hazardous interactions affecting autonomous ships navigation

A method to identify and rank objects and hazardous interactions affecting autonomous ships navigation

Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs

Constructing Knowledge Maps for Situation Awareness of Maritime Autonomous Surface Ships

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