Deep Q-Learning-Based Trajectory Optimization for Vehicle Navigation in CARLA
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Date
2024-06-01
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ALGERIAN JOURNAL OF SIGNALS AND SYSTEMS (AJSS).Vol. 9, Issue 2.pp 128-133
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 QLearning 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.