There are 6 repositories under carla topic.
Open-source simulator for autonomous driving research.
👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla
Traffic scenario definition and execution engine
Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.
Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"
Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:
(CoRL 2019) Driving in CARLA using waypoint prediction and two-stage imitation learning
Be Driven 🚘
Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach. ICCV 2021.
Implementation of the real-time MPC based on iLQR in Carla simulator
Blender add-on for creating OpenDRIVE and OpenSCENARIO based automotive driving scenarios including 3D models
converter for OpenStreetMaps to OpenDrive roads - for use with Carla or other things
Predict Vehicle collision moments before it happens in Carla!. CNN and LSTM hybrid architecture is used to understand a series of images.
How to run CARLA simulator on colab
Tools for dataset generation based on CARLA simulator. (Data Collector)
通过carla-ros-bridge在carla上实现自动驾驶planning and control。
reinforcement learning based agents for self-driving in CARLA
Carla Imitation Learning Trainer
Collision Avoidance System for Self-Driving Vehicles by Delta Autonomy, Robotics Institute, CMU
This project implements a functional motion planning stack for autonomous vehicles to avoid both static and dynamic obstacles while tracking the center line of a lane, while also handling stop signs.
Module for detecting traffic lights in the CARLA autonomous driving simulator. Based on the YOLO v2 deep learning object detection model and implemented in keras, using the tensorflow backend.
2d Deep Reinforcement Learning environment with Carla
A simple gym environment wrapping Carla, a simulator for autonomous driving research. The environment is designed for developing and comparing reinforcement learning algorithms. Trackable costs also enable the application of safe reinforcement learning algorithms.
Implementation of motion planning for self-driving cars based on EM Planner in Carla.
applying multi-agent reinforcement learning for highway-merging autonomous vehicles
Create a dataset to train a lane detection neural network with CARLA