Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA

• URL: http://arxiv.org/abs/2311.10735v1
• Date: Mon, 23 Oct 2023 04:23:07 GMT
• Title: Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA
• Authors: Ghadi Nehme, Tejas Y. Deo
• Abstract summary: The goal of this project is to train autonomous vehicles to make decisions to navigate in uncertain environments using deep reinforcement learning techniques. The simulator provides a realistic and urban environment for training and testing self-driving models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous vehicles have the potential to revolutionize transportation, but they must be able to navigate safely in traffic before they can be deployed on public roads. The goal of this project is to train autonomous vehicles to make decisions to navigate in uncertain environments using deep reinforcement learning techniques using the CARLA simulator. The simulator provides a realistic and urban environment for training and testing self-driving models. Deep Q-Networks (DQN) are used to predict driving actions. The study involves the integration of collision sensors, segmentation, and depth camera for better object detection and distance estimation. The model is tested on 4 different trajectories in presence of different types of 4-wheeled vehicles and pedestrians. The segmentation and depth cameras were utilized to ensure accurate localization of objects and distance measurement. Our proposed method successfully navigated the self-driving vehicle to its final destination with a high success rate without colliding with other vehicles, pedestrians, or going on the sidewalk. To ensure the optimal performance of our reinforcement learning (RL) models in navigating complex traffic scenarios, we implemented a pre-processing step to reduce the state space. This involved processing the images and sensor output before feeding them into the model. Despite significantly decreasing the state space, our approach yielded robust models that successfully navigated through traffic with high levels of safety and accuracy.

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