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• W4312223234 endingPage "317" @default.
• W4312223234 startingPage "317" @default.
• W4312223234 abstract "The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges." @default.
• W4312223234 created "2023-01-04" @default.
• W4312223234 creator A5017149914 @default.
• W4312223234 creator A5033886992 @default.
• W4312223234 creator A5068350993 @default.
• W4312223234 creator A5072593082 @default.
• W4312223234 creator A5083202932 @default.
• W4312223234 date "2022-12-28" @default.
• W4312223234 modified "2023-09-27" @default.
• W4312223234 title "How Do Autonomous Vehicles Decide?" @default.
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• W4312223234 doi "https://doi.org/10.3390/s23010317" @default.
• W4312223234 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36616915" @default.
• W4312223234 hasPublicationYear "2022" @default.

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