Article: A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique Journal: International Journal of Electric and Hybrid Vehicles (IJEHV) 2024 Vol.16 No.2 pp.103 - 135 Abstract: This manuscript proposes a hybrid approach for an autonomous vehicle (AV) control system to improve the robustness of vehicles. The proposed hybrid technique is the combination of honey badger algorithm (HBA) and radial basis function neural network (RBFNN), together known as HBA-RBFNN technique. The major purpose of the proposed method is detecting the real-time signal by detecting the intermittence and abruption amid two autonomous vehicle (AV) systems to enhance safety. The proposed approach is applied to control the speed and acceleration of the system. The distance-gap is controlled by reference acceleration by the controller of proportional integral derivative (PID). The proposed approach carries out the best tuning of the proposed approach and the control signal is produced using the HBA approach, and the best signal is predicted by using the RBFNN approach. Then, the performance of the proposed approach is done in MATLAB and compared with existing approaches. The proposed method optimally controls the vehicle and increases the robustness of the system. From the simulation outcome, the proposed method gives less settling time and is better than the existing approaches. Inderscience Publishers - linking academia, business and industry through research

Title: A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique

Authors: Ashwin Kavasseri Venkitaraman; Venkata Satya Rahul Kosuru

Addresses: Department of Electrical and Electronics Engineering, University of Cincinnati, Ohio, USA ' Department of Electrical and Computer Engineering, Lawrence Technological University, Southfield, Michigan, USA

Abstract: This manuscript proposes a hybrid approach for an autonomous vehicle (AV) control system to improve the robustness of vehicles. The proposed hybrid technique is the combination of honey badger algorithm (HBA) and radial basis function neural network (RBFNN), together known as HBA-RBFNN technique. The major purpose of the proposed method is detecting the real-time signal by detecting the intermittence and abruption amid two autonomous vehicle (AV) systems to enhance safety. The proposed approach is applied to control the speed and acceleration of the system. The distance-gap is controlled by reference acceleration by the controller of proportional integral derivative (PID). The proposed approach carries out the best tuning of the proposed approach and the control signal is produced using the HBA approach, and the best signal is predicted by using the RBFNN approach. Then, the performance of the proposed approach is done in MATLAB and compared with existing approaches. The proposed method optimally controls the vehicle and increases the robustness of the system. From the simulation outcome, the proposed method gives less settling time and is better than the existing approaches.

Keywords: autonomous vehicle; AV; robustness; acceleration; velocity; speed; proportional integral derivative controller; gain parameters.

DOI: 10.1504/IJEHV.2024.138989

International Journal of Electric and Hybrid Vehicles, 2024 Vol.16 No.2, pp.103 - 135

Received: 27 Jan 2023
Accepted: 05 Nov 2023
Published online: 06 Jun 2024 *

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Title: A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique

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