Source codes of “Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method” published in IEEE Trans. Intelligent Transportation Systems

The main goal of the project is to implement in C++ Model Predictive Control to drive the vehicle around the simulator track.

This project aims to provide a data and control bridge for the communication between the latest version of Apollo and Carla.

A curated list of Resources for Motion Planning

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics

Deep Understanding of Traffic Scenes for Autonomous Driving

Numenta published papers code and data

Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

This is the code for "How to Simulate a Self-Driving Car" by Siraj Raval on Youtube

Note for Apollo 2.0/2.5 perception, prediction and planning modules

Production-Grade Container Scheduling and Management

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

The CarND Capstone Project

Open-source software for urban autonomous driving