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

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.