Optimization of autonomous vehicle speed control mechanisms using hybrid DDPG-SHAP-DRL-stochastic algorithm. (November 2022) - Syavasya, C.V.S.R.; Muddana, A. Lakshmi
Highlights: Autonomous Vehicles (AV) are the future milestones of the automobile industry, which functions without the intervention of human being. Numerous researches have been stimulated by leading automobile sectors of the world, to address the anticipated challenges in implementing the autonomous vehicles in a practical scenario. The speed control mechanism is the predominant challenge which acts in the basis of Machine Learning mechanism is the major thrust area associated with autonomous vehicles. This research work introduces a novel hybrid algorithm composed of Deep Deterministic Policy Gradient (DDPG) - SHapley Additive exPlanations (SHAP) – Deep Reinforcement Learning (DRL)-stochastic algorithm. Abstract: Autonomous Vehicles (AV) are the future milestones of the automobile industry, which functions without the intervention of human being. Numerous researches have been stimulated by leading automobile sectors of the world, to address the anticipated challenges in implementing the autonomous vehicles in a practical scenario. The speed control mechanism is the predominant challenge which acts in the basis of Machine Learning mechanism is the major thrust area associated with autonomous vehicles. Reinforcement Learning (RL) is the effective algorithm to solve the challenges associated with the autonomous driving of vehicles and its decision on complex scenarios. A simulative environment is advantageous for training and validation of an RL algorithm because it reduces risk and saves resources. This research work introduces a novel hybrid algorithm composed of Deep Deterministic Policy Gradient (DDPG) - SHapley Additive exPlanations (SHAP) – Deep Reinforcement Learning (DRL)-stochastic algorithm. The primary objective of this research work is to introduce an RL environment for optimizing longitudinal control.