• M Satwika
• I. S. Srinivasa Rao

Regarding the drive-control plan for self-driving automobiles, the present approach relies on exterior elements like the pedestrian walk indicators and the surfaces of the roads that obscure the internal status significantly. This study with the title ‘A Driving Decision Strategy (DDS) based on Machine Learning for an Autonomous Vehicle’ goes further by providing a complex model, which takes into consideration the generic external environment and the decision process to identify the most suitable course of the action plans for the self-driving automobiles. In deciding which driving amongst the other result in the best result, this proposed DDS include the genetic system with the cloud-based sensor data acquisition system. In regard to this paper, the applicability of DDS was evaluated in terms of performance difference against conventional models including RF and MLP. Consequently, the study indicates that using DDS provides better accuracy and therefore communicates data at a significantly higher rate than those stated models above. It also proved that DDS is for a 5% normal system error better than other current autonomous systems existent; Besides, revolutionary per mins (RPMs), individual speed, halting distance, steering angle, and lane alteration; It also performs much faster than multi-line perceptron (MLP) by 40% and multifariousness faster than random forest (RF) by 22%. Here, the DDS encodes the sensor data using a genetic algorithm to attain the best parameters that would translate into exploitable positions that the driver can easily make a decision on. Similar changes if done, advocate for future enhancements.

DDS, Genetic Algorithm, Random Forest(RF), Multi-Layer Perceptron(MLP), Autonomous Vehicle.

"Driving Decision Strategy Based on Machine Learning For an Autonomous Vehicle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.h23-h29, July-2024, Available :http://www.jetir.org/papers/JETIR2407703.pdf

"Driving Decision Strategy Based on Machine Learning For an Autonomous Vehicle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pph23-h29, July-2024, Available at : http://www.jetir.org/papers/JETIR2407703.pdf