Deep Reinforcement Learning for Real-Time Autonomous Vehicle Control

Abstract

Autonomous vehicle control in real-time environments demands a system that can adapt to dynamic conditions, including traffic, obstacles, and changing road scenarios. Traditional rule-based and model-driven control methods face limitations in handling the complexity and variability of real-world driving environments. This paper explores the application of deep reinforcement learning (DRL) for real-time autonomous vehicle control. By leveraging DRL, we develop a control framework that enables autonomous vehicles to learn optimal driving policies through interaction with their environment. The proposed method employs deep neural networks to process high-dimensional sensory data, such as camera images and LiDAR inputs, and generate control actions, such as steering, acceleration, and braking, in real-time. Experimental results demonstrate the model's ability to achieve robust performance in complex driving scenarios, outperforming conventional control methods in terms of safety, efficiency, and adaptability.