gustavomoers/CollisionAvoidance-Carla-DRL-MPC
A hybrid collision avoidance system combining Deep Reinforcement Learning with Model Predictive Control, designed for autonomous vehicles in CARLA to navigate scenarios with stationary obstacles.
This project helps autonomous vehicle engineers develop and test advanced collision avoidance systems. It takes information about a car's position, speed, and surrounding stationary obstacles within the CARLA simulator. It then outputs a safe driving path and controls for the vehicle to avoid collisions, demonstrating how a car can navigate around parked vehicles.
No commits in the last 6 months.
Use this if you are an autonomous vehicle engineer working on developing or evaluating collision avoidance strategies for self-driving cars, especially in simulation environments like CARLA.
Not ideal if you are looking for a system to manage dynamic obstacles or complex urban traffic scenarios, as this is designed for stationary obstacle avoidance.
Stars
58
Forks
4
Language
Python
License
MIT
Category
Last pushed
May 08, 2024
Commits (30d)
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