gustavomoers/E2E-CARLA-ReinforcementLearning-PPO
An end-to-end (E2E) reinforcement learning model for autonomous vehicle collision avoidance in the CARLA simulator, using a recurrent PPO algorithm for dynamic control. The model processes RGB camera inputs to make real-time acceleration and steering decisions.
This helps with developing and testing autonomous vehicle collision avoidance systems in a simulated environment. It takes visual data from a virtual car's camera and outputs real-time steering and acceleration commands to prevent crashes. Autonomous vehicle researchers, engineers, and simulation specialists would find this useful for training AI models.
No commits in the last 6 months.
Use this if you are a researcher or engineer working on autonomous driving and need to train an AI model to avoid collisions using camera input in a simulated environment.
Not ideal if you are looking for a ready-to-deploy physical autonomous driving solution or a simulation for traffic flow analysis rather than AI training.
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41
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6
Language
Python
License
MIT
Category
Last pushed
Apr 12, 2024
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0
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