Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment

Guo, Jian; Luo, Cheng; Ma, Kaijiang

doi:10.1016/j.ress.2022.108891

Cited by 31 publications

(3 citation statements)

References 52 publications

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“…Firstly, autonomous vehicles need to accurately perceive and understand complex road environments (Guo et al, 2023 ). This includes accurate perception and recognition of other vehicles, pedestrians, traffic signals, road signs, and geometric structures.…”

Section: Related Workmentioning

confidence: 99%

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

Wu,

Wang,

Shen

2023

Front. Neurorobot.

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With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

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Section: Related Workmentioning

confidence: 99%

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

Wu,

Wang,

Shen

2023

Front. Neurorobot.

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“…These studies aim to achieve dynamic control of coupled risks. Combining the N-K model with different research methods has also been used to enhance the risk coupling model, such as the AHP-based N-K model for constructing a risk coupling model of transportation accidents in complex marine environments [ 24 ]. The BBN-NK model has been utilized for risk analysis of tunnel fire accidents, quantifying various risk factors and simulating the frequency of tunnel fire accidents [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the evolutionary control of unsafe behavior of construction personnel based on multi-field coupled-homogeneous analysis model

Zhao,

Wang,

Zheng

et al. 2024

PLoS ONE

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Unsafe behavior among construction personnel poses significant risks in petroleum engineering construction projects. This study addresses this issue through the application of a multi-field coupled homogeneous analysis model. By conducting case analyses of petroleum engineering construction accidents and utilizing the WSR methodology, the influencing factors of unsafe behaviors among construction personnel are systematically categorized into organizational system factors, equipment management factors, and construction personnel factors. Subsequently, employing Risk coupling theory, the study delves into the analysis of these influencing factors, discussing their coupling mechanisms and classifications, and utilizing the N-K model to elucidate the coupling effect among them. Furthermore, a novel approach integrating coupling analysis and multi-agent modeling is employed to establish an evolutionary model of construction personnel’s unsafe behavior. The findings reveal that a two-factor control method, concurrently reinforcing equipment and construction personnel management, significantly mitigates unsafe behavior. This study provides valuable insights into the evolution of unsafe behavior among construction personnel and offers a robust theoretical framework for targeted interventions. Significantly, it bears practical implications for guiding safety management practices within petroleum engineering construction enterprises. By effectively controlling unsafe behaviors and implementing targeted safety interventions, it contributes to fostering sustainable development within the petroleum engineering construction industry.

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“…In the fields such as natural disasters, safety management, public health, and even national security areas, risk coupling has been extensively studied. Risk coupling is derived from physical control theory and system theory, which generally means the interaction among various risk factors in the system (Guo et al., 2023). The endogenous mechanism of risk coupling comes from the complex system's brittleness, risk coherence, and risk fluctuation principles.…”

Section: Introductionmentioning

confidence: 99%

Risk coupling analysis under accident scenario evolution: A methodological construct and application

Yao,

Zhang,

Wang

et al. 2023

Risk Analysis

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Dynamic processes in various fields exhibit risk coupling phenomena, but existing risk analysis studies tend to ignore the risk coupling effects of dynamic scenarios. Considering the principles of digitization, objective quantification, and the full process that should be adopted in the risk coupling analysis, an integrated risk coupling analysis framework is proposed. Specifically, the weighted Eclat algorithm is used to mine the risk association rules, then the key risk factors are extracted by social network analysis, and the stochastic Petri net is used to complete the construction, simulation, and evolution of accident scenarios. This universal framework can analyze the risk phenomena of accident scenario evolution in a process‐oriented manner and decouple risks based on key risk factors and disconnect the chain of the accident scenario evolution process. Finally, the proposed framework is applied to the coupled analysis of fire risk in Chinese urban communities to verify its feasibility and scientific validity.

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Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment

Cited by 31 publications

References 52 publications

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

Research on the evolutionary control of unsafe behavior of construction personnel based on multi-field coupled-homogeneous analysis model

Risk coupling analysis under accident scenario evolution: A methodological construct and application

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