Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a neuro-fuzzy controller for intelligent cruise control of semiautonomous vehicles.  addresses the problem of longitudinal control that aims at regulating the speed of the controlled vehicle in order to maintain constant time headway with respect to the vehicle in front. A fuzzy radial basis function network (FRBFN) longitudinal controller is designed to incorporate the merits of fuzzy logics as ll as neural networks. The FRBFN is prestructured, and its parameters are configured such that they are associated with their physical meaning. The parameters of the output layer are learned online via gradient algorithm. An attractive feature of the proposed method is that it does not require the training data and the vehicle longitudinal dynamic model. Simulation results on a vehicle theoretical model are provided to demonstrate the effectiveness of this controller. In order to investigate the proposed control algorithms in real-life situations, a small-scaled vehicle with computer and sensors onboard is developed. Experimental results of a conventional PID controller and the FRBFN controller are provided for comparison.
['Lin Cai', 'Ahmad B. Rad', 'Wai-Lok Chan']
I am writing a research paper on a idea An Intelligent Longitudinal Controller for Application in Semiautonomous Vehicles
[{"from": "human", "value": "I am writing a research paper on a idea An Intelligent Longitudinal Controller for Application in Semiautonomous Vehicles"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a neuro-fuzzy controller for intelligent cruise control of semiautonomous vehicles.  addresses the problem of longitudinal control that aims at regulating the speed of the controlled vehicle in order to maintain constant time headway with respect to the vehicle in front. A fuzzy radial basis function network (FRBFN) longitudinal controller is designed to incorporate the merits of fuzzy logics as ll as neural networks. The FRBFN is prestructured, and its parameters are configured such that they are associated with their physical meaning. The parameters of the output layer are learned online via gradient algorithm. An attractive feature of the proposed method is that it does not require the training data and the vehicle longitudinal dynamic model. Simulation results on a vehicle theoretical model are provided to demonstrate the effectiveness of this controller. In order to investigate the proposed control algorithms in real-life situations, a small-scaled vehicle with computer and sensors onboard is developed. Experimental results of a conventional PID controller and the FRBFN controller are provided for comparison."}]