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A Comparative Analysis Between Proposed Neuro-fuzzy, Fuzzy, and Heuristic-Neuro-fuzzy Controller for Autonomous Vehicle Parking in the Dynamic Environment


The single-stage control is generally used to solve the autonomous parking system for dynamic environment. Sometimes, environment provides conflicting information to sensors where the single control mechanism may not be able to take prompt action. This situation causes a multiple-choice overload problem in which a single-stage system becomes confused when many decisions can be true, which can lead to collision in exceptional situations. This paper proposes a multi-stage neuro-fuzzy architecture for autonomous parallel parking in the unknown and dynamic environment. It generates an obstacle avoidance capability for the vehicle during the parking process. The multiple-choice overload problem is addressed, and a possible solution is provided by aiding a trained neural network as a pre-controller to the main fuzzy controller. The simulations results in the presence of static and moving obstacle are provided and compared with the earlier methods to prove the validity of the proposed architecture.

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Correspondence to Naitik M. Nakrani.

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This article is part of the topical collection “Smart and Connected Electronic Systems” guest edited by Amlan Ganguly, Selcuk Kose, Amit M. Joshi, and Vineet Sahula.

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Nakrani, N.M., Joshi, M.M. A Comparative Analysis Between Proposed Neuro-fuzzy, Fuzzy, and Heuristic-Neuro-fuzzy Controller for Autonomous Vehicle Parking in the Dynamic Environment. SN COMPUT. SCI. 3, 487 (2022).

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  • Autonomous parallel parking
  • Control system design
  • Neuro-fuzzy
  • Dynamic environment