ACDS—Assisted Cooperative Decision-Support for Reliable Interaction based Navigation Assistance for Autonomous Vehicles

Abstract Autonomous vehicles (AV) technology is designed for replacing conventional transportation systems ahead of energy conservation, pollution control, accident prevention, etc. The benefits of AV are feasible by the incorporation of information technology and connected infrastructures. These provide seamless support for driving and navigation assistance with the knowledge of the environment. However, the vehicles\u0027 independent assistance relies on the cooperative nature of the neighbors and infrastructure units. In this article, assisted cooperative decision-support (ACDS) is proposed for improving the spontaneous decisions for vehicle connectivity and navigation issues. The interrupt due to multiple connectivity issues from a specific infrastructure region is addressed for leveraging the decision support for AVs. In this decision-making process, neural learning is used for improving the analysis of radial inputs. The learning process categorizes connectivity and outage information based on the assisted navigation ratio between the neighbors and infrastructures. This helps to provide flexible, cooperative analysis in different navigation scenarios, promoting accuracy, and reducing the input complexity. The performance of ACDS is verified using outage, complexity, analysis time, and accuracy measures.