Deep Q-Learning-Based Trajectory Optimization for Vehicle Navigation in CARLA

Abstract

This paper presents a comprehensive study that focuses on simulating a vehicle within the autonomousvehicle simulator, CARLA. The primary objective of this research is to enable the vehicle to accurately follow apredetermined trajectory while effectively avoiding obstacles in its environment. Deep Q-Learning algorithms areemployed to achieve this goal, aiming to optimize the safety of the vehicle'snavigation. The simulation of the vehicleserves as a platform for studying the rules of Deep Q-Networks (DQN) and their impact on the vehicle's navigation.The objective is to identify the most suitable rule that leads to improved optimization of the vehicle's trajectory. Byleveraging the capabilities of CARLA as the simulation environment and implementing state-of-the-art DQNalgorithms, this research contributes to the advancement of autonomous vehicle technology. The findings of this studyhave practical implications for enhancing the safety and efficiency of autonomous vehicle navigation systems, makingthem highly relevant to industryprofessionals, researchers, and academic scholars in this field.