Effective Q-Based Learning Approaches for Advanced Path Planning and Trajectory Generation in Autonomous Vehicles

Abstract

Autonomous vehicles are a groundbreaking technology set to substantially alter transportation systems. Efficient route mapping and trajectory creation are essential for the secure and effective functioning of these vehicles.  In this context, Q-based learning, specifically Q-learning, emerges as a promising approach to equip these vehicles with the ability to autonomously navigate complex and dynamic environments. This paper explores the integration of Q-learning into the domain of autonomous vehicle navigation. The methodology involves defining a state space that encapsulates critical information about the vehicle's surroundings, an action space that encompasses permissible vehicle maneuvers, and a reward function that guides the learning process by quantifying desirable outcomes and penalties. The Q-learning algorithm, which iteratively updates Q-values using the Bellman equation, enables autonomous vehicles to learn optimal policies for path planning and trajectory generation. Beyond theoretical considerations, research outcomes highlights the practical challenges associated with the real-world deployment of Q-based learning systems, emphasizing the need for continuous safety mechanisms, simulation-based testing, and parameter tuning. Additionally, it underscores the adaptability of Q-learning to handle continuous state and action spaces through methods like deep reinforcement learning.