چکیده مقاله
Enhancing autonomous vehicles AVs ensures a safe and reliable transportation system Achieving level 5 autonomy, as per the Society of Automotive Engineers SAE classification, requires AVs to navigate through complex and unconventional traffic environments Path following, a key aspect of automated driving, involves guiding a vehicle accurately and safely along a predefined path Traditional path following methods often rely on parameter adjustments or rule based approaches, which may not be suitable for dynamic or intricate environments Reinforcement learning RL has emerged as a promising technique capable of learning effective control strategies from an agent's experiences This study investigates the effectiveness of the Deep Deterministic Policy Gradient DDPG method for controlling acceleration and the Deep Q Network DQN technique for controlling steering in AV path following The combination of the DDPG and DQN algorithms together demonstrates rapid convergence, allowing the agent to achieve stable and efficient path following while maintaining smooth control without excessive actions The results indicate the efficiency of the new approach, suggesting its potential contribution to the advancement of automated driving technology
کلیدواژهها
نویسندگان
Ali Rizehvandi
Faculty of Mechanical Engineering K N Toosi University of Technology Tehran, Iran
Shahram Azadi
Faculty of Mechanical Engineering K N Toosi University of Technology Tehran, Iran
شیوه ارجاع
Rizehvandi, Ali and Azadi, Shahram,1403,Path-following control for autonomous vehicles utilizing both DDPG and DQN algorithms,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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