护理学 | SCI期刊专刊信息1条

全文截稿: 2021-04-15

影响因子: 3.619

中科院JCR分区:

• 大类 : 管理科学 - 2区

• 小类 : 工程：工业 - 2区

• 小类 : 运筹学与管理科学 - 2区

网址: https://www.journals.elsevier.com/safety-science

Deep learning approaches with the benefits of enhancing efficiency and improving accuracy has been widely used in both of academia and industry. From a safety science in transportation point of view, the deep learning approaches bring about both opportunities and challenges for transportation applications. On the one hand, deep learning approaches can help interested parties to better protect safety in dangerous traffic situations, improving the state-of-the-art of safety solutions. On the other hand, deep learning approaches including perception, neural network, machine learning, knowledge representation and so on, has been making revolutions in various areas, such as autonomous vehicles, robotic manipulators, image analysis, computer vision, natural language processing, time-series analysis, and target online advertisement. This has made deep learning approaches a promising tool to be utilized in modelling and optimization of chemical processes. For example, deep learning has been shown to be effective in reconstructing missing information of damaged image. Deep neural networks have also been widely used to restore information in ill-posed situation.

According to Global Status Report on Road Safety 2018 issued by World Health Organization, every year approximately 1.35 million people die as a consequence of road accidents. The burden of road traffic injuries and deaths is disproportionately borne by VRUs, who contribute to half of all victims. In other words, almost 50% of road fatalities are VRUs, who are cyclists and pedestrians, non-motorized road users. The World Health Organization predicts that by the year 2020 road accidents will become the leading cause of premature death. To avoid accidents and protect VRU from danger, it is important to detect VRUs and to predict their intentions.

This special issue solicits novel contributions on all aspects of theoretical and applied studies in addressing VRUs safety including cybersecurity, public transit planning and operation issues in the context of automation, electrification and personalization based on deep learning and artificial intelligence. Topics of interest to this special issue include, but are not limited to:

Deep learning based autonomous vehicle and pedestrian/cyclists interaction

Surrogate safety measures of pedestrians and cyclists

Artificial intelligence based Vulnerable road user’s safety modeling