Enhancing Maritime Safety: Integrating Human Thinking into Intelligent Ship Collision Avoidance

By Ashutosh RoyReviewed by Susha Cheriyedath, M.Sc.Aug 9 2023

In an article published in the Journal of Ocean Engineering, the researchers proposed a novel methodology that integrates human thinking experiences into intelligent ship collision avoidance models inspired by cognitive processes. This approach encompasses both fast and slow thinking patterns, enhancing interpretability, efficiency, and adaptability in collision avoidance strategies.

In modern maritime navigation, the integration of intelligent technologies has revolutionized the industry. Autonomous navigation and advanced decision-making have become crucial components of ship operations, enabling the realization of various projects that showcase the seamless integration of intelligent ships into everyday maritime activities. The International Maritime Organization (IMO) has categorized ship autonomy into four levels to ensure the safe and efficient incorporation of these intelligent systems. At each level, the need for effective human-machine collaboration and the interpretability of decision algorithms becomes increasingly significant. This collaboration and cooperation between humans and machines are key to achieving smooth transitions and successful navigation of intelligent ships.

Existing approaches and limitations

Over the years, numerous approaches have been explored to tackle the intricate problem of collision avoidance. These approaches span from heuristic planning techniques to sophisticated learning-based algorithms. The primary focus of these methods is to enhance the effectiveness of collision avoidance, optimize execution efficiency, and foster cooperative decision-making between human operators and intelligent systems. Despite the progress made, many of these solutions exhibit certain limitations. Some employ intricate model structures that can compromise efficiency, while others are overly reliant on specific environmental scenarios. Moreover, a common shortcoming is the insufficient incorporation of human insight and wisdom into these models, potentially hindering their adaptability and overall efficacy.

Integrating human thinking experience

To bridge the gap between machine intelligence and human understanding, a groundbreaking methodology has been proposed. This novel approach integrates human thinking experiences into intelligent collision avoidance models. Drawing inspiration from the cognitive processes underpinning human decision-making, the model is meticulously crafted to emulate fast and slow thinking patterns. The concept of fast thinking, characterized by intuition and rapid decision-making, finds expression as a basic collision avoidance model.

On the other hand, the notion of slow thinking involving thorough analysis and reasoned decision-making is embodied in an analytical model. These two distinct models correspond to different collision avoidance strategies, enhancing the model's versatility and adaptability across various scenarios.

Advantages of this design

Enhanced interpretability: By aligning the model with the natural patterns of human thinking, the resulting model becomes more comprehensible to operators and stakeholders. This increased interpretability directly bolsters their trust in the algorithmic decisions made by the intelligent system.

Efficient collision avoidance: Incorporating human decision-making experiences streamlines the collision avoidance process. This approach avoids overly complex designs that could potentially hinder the execution efficiency of the intelligent system, ensuring a smooth and responsive navigation experience.

Flexibility and accessibility: A critical element of this methodology is the introduction of an evaluation model grounded in field theory. This model seamlessly merges human experiences with algorithmic advantages, facilitating optimal decision-making in scenarios characterized by complexity and uncertainty.

Algorithm architecture

The heart of the proposed methodology lies in the carefully crafted algorithm architecture. This architecture strategically encompasses both fast and slow thinking patterns. The basic model draws upon established Collision Regulations (COLREGS) and the cumulative navigation experience to address straightforward encounter situations effectively. In contrast, the analytical model takes center stage when dealing with intricate scenarios involving multiple ships. Within the analytical model, the integration of the repulsive force and driving force components of the social force model enables fine-tuned course adjustments and subtle fluctuations, ensuring a highly responsive and adaptive collision avoidance strategy.

Case studies and future prospects

To empirically validate the effectiveness of the collision avoidance model, a comprehensive array of case studies was conducted. These case studies encompassed various encounter scenarios, effectively assessing the model's performance and robustness. The outcomes of these studies unequivocally demonstrated the efficacy of the proposed architecture. Furthermore, the potential applications of this model extend beyond collision avoidance. The model's adaptability and accuracy make it a promising candidate for advancing collision-free trajectory navigation in complex multi-ship situations.

Conclusion

Integrating human thinking experiences into intelligent collision avoidance models offers a profound promise of enhancing trust, autonomy, and collaboration in the maritime industry. By aligning with the cognitive patterns inherent in human decision-making, these models transcend the divide between humans and machines, providing interpretable and efficient collision avoidance solutions. As the maritime industry continues its journey towards greater autonomy, this innovative methodology is a robust foundation for achieving safe, efficient, and cooperative ship navigation.

While the current incarnation of the model showcases remarkable results, the path forward invites further refinements and adaptations to diverse real-world scenarios. This groundbreaking approach lays the groundwork for enhanced human-machine collaboration in maritime navigation, ushering in a new era of safer and more efficient maritime operations.