👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla

Deep Reinforcement Learning (PPO) in Autonomous Driving (Carla) [from scratch]

[NeurIPS 2022] Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline.

Official github page of UCF SST CitySim Dataset

Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch

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:

Tools for dataset generation based on CARLA simulator. (Data Collector)

Code for the paper "Reinforced Curriculum Learning for Autonomous Driving in CARLA" (ICIP 2021)

Self Driving Cars Longitudinal and Lateral Control Design

TELECARLA: An Open Source Extension of the CARLA Simulator for Teleoperated Driving Research Using Off-the-Shelf Components

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.

Saving incoming camera sensor images data as Numpy arrays to generate ground truth data for semantic segmentation

CARLA, açık kaynaklı bir otonom sürüş simülatörüdür.

Learning Model Predictive Control (LMPC) for autonomous racing in CARLA 3D environment.

[WACV 2025] Official code of "SEED4D: A Synthetic Ego-Exo Dynamic 4D Data Generator, Driving Dataset and Benchmark"

Implementation of a Longitudinal and Lateral controller (2D) of an autonomous vehicle on Carla Simulator

A simple yet effective repo for object detection based on the FCOS architecture.

This is the ultimate step-by-step guide for the final Project work of Coursera's Introduction to Self-Driving Car's Course on Carla Driving Simulator for Trajectory Tracking and PID controlling.

🚘⚠️ RAAS is a modular driver assistance system developed in the CARLA simulator for integration into Russian vehicles such as Lada, Aurus, and others. It includes a wide range of ADAS features and proposes a concept and prototype of how such a system could be built for domestic cars, focusing on adaptability, open architecture, and real-world use.

An implementation of a full motion and behavior planning pipeline for a self-driving car in the CARLA simulator.