
<abstract> <p>Estimating the state of surrounding vehicles is crucial to either prevent or avoid collisions with other road users. However, due to insufficient historical data and the unpredictability of future driving tactics, estimating the safety status is a difficult undertaking. To address this problem, an intelligent and autonomous traffic management system based on V2V technology is proposed. The main contribution of this work is to design a new system that uses a real-time control system and a fuzzy logic algorithm to estimate safety. The robot operating system (ROS) is the foundation of the control architechture, which connects all the various system nodes and generates the decision in the form of a speech and graphical message. The safe path is determined by a safety evaluation system that combines sensor data with a fuzzy classifier. Moreover, the suitable information processed by each vehicle unit is shared in the group to avoid unexpected problems related to speed, sudden braking, unplanned deviation, street holes, road bumps, and any kind of street issues. The connection is provided through a network based on the ZigBee protocol. The results of vehicle tests show that the proposed method provides a more reliable estimate of safety as compared to other methods.</p> </abstract>
Artificial intelligence, robot operating system, FOS: Mechanical engineering, Vehicular Ad Hoc Networks, Real-time computing, Visual arts, Intelligent Transportation Systems, Internet of Vehicles, Engineering, Architecture, QA1-939, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Embedded system, T57-57.97, Computer network, Applied mathematics. Quantitative methods, Urban Driving, safety estimation, Path (computing), Computer science, Fuzzy logic, v2v, Control and Systems Engineering, Automotive Engineering, Physical Sciences, real-time system, Vehicular Ad Hoc Networks and Communications, fuzzy logic, Autonomous Vehicle Technology and Safety Systems, V2X Communications, Classifier (UML), Modeling and Control of Traffic Flow Systems, Mathematics, FOS: Civil engineering, Art
Artificial intelligence, robot operating system, FOS: Mechanical engineering, Vehicular Ad Hoc Networks, Real-time computing, Visual arts, Intelligent Transportation Systems, Internet of Vehicles, Engineering, Architecture, QA1-939, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Embedded system, T57-57.97, Computer network, Applied mathematics. Quantitative methods, Urban Driving, safety estimation, Path (computing), Computer science, Fuzzy logic, v2v, Control and Systems Engineering, Automotive Engineering, Physical Sciences, real-time system, Vehicular Ad Hoc Networks and Communications, fuzzy logic, Autonomous Vehicle Technology and Safety Systems, V2X Communications, Classifier (UML), Modeling and Control of Traffic Flow Systems, Mathematics, FOS: Civil engineering, Art
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