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awesome-autonomous-vehicles

Curated List of Self-Driving Cars and Autonomous Vehicles Resources
https://github.com/manfreddiaz/awesome-autonomous-vehicles

Last synced: 21 days ago
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Companies

Computer Vision

Courses

Computer Vision

  • [Coursera - presented by Andrew Ng, as of 2020 Jan 28 it has 125,344 ratings and 30,705 reviews.
  • [Coursera+DeepLearning.ai - presented by Andrew Ng, 5 Courses, teaches foundations of deep learning, programming language: python
  • [Udacity - teaches the skills and techniques used by self-driving car teams. Program syllabus can be found here.
  • [INRIA - Introduces the key concepts required to program mobile robots and autonomous vehicles. The course presents both formal and algorithmic tools, and for its last week's topics (behavior modeling and learning), it will also provide realistic examples and programming exercises in Python.
  • [Universty of Glasgow - Introduces the concepts behind autonomous vehicle guidance and coordination and enables students to design and implement guidance strategies for vehicles incorporating planning, optimising and reacting elements.
  • [David Silver - Udacity - driving cars coming from a Software Engineering background.
  • [Stanford - Contains a simple self-driving project and simulator.
  • [MIT - "This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. (...)"
  • [MIT - "This page is a collection of MIT courses and lectures on deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence organized by Lex Fridman."
  • [MIT - "Human-Centered AI at MIT is a collection of research and courses focused on the design, development, and deployment of artificial intelligence systems that learn from and collaborate with humans in a deep, meaningful way."
  • [UCSD - A hands-on, project-based course using DonkeyCar with lane-tracking functionality and various advanced topics such as object detection, navigation, etc.
  • [MIT - Class about the science of autonomy at the graduate level. This is a hands-on, project-focused course focusing on self-driving vehicles and high-level autonomy. The problem: Design the Autonomous Robo-Taxis System for the City of Duckietown.
  • [Coursera - A 4 course specialization about Self-Driving Cars by the University of Toronto. Covering all the way from the Introduction, State Estimation & Localization, Visual Perception, Motion Planning.
  • [Udacity - teaches the skills and techniques used by self-driving car teams. Program syllabus can be found here.
  • [Stanford - Contains a simple self-driving project and simulator.

Datasets

Computer Vision

  • Udacity - Udacity driving datasets released for Udacity Challenges. Contains ROSBAG training data. (~80 GB).
  • Comma.ai - 7 and a quarter hours of largely highway driving. Consists of 10 videos clips of variable size recorded at 20 Hz with a camera mounted on the windshield of an Acura ILX 2016. In parallel to the videos, also recorded some measurements such as car's speed, acceleration, steering angle, GPS coordinates, gyroscope angles. These measurements are transformed into a uniform 100 Hz time base.
  • Oxford Radar RobotCar - radar extension to The Oxford RobotCar Dataset providing data from a Navtech CTS350-X Millimetre-Wave FMCW radar and Dual Velodyne HDL-32E LIDARs with optimised ground truth radar odometry for 280 km of driving.
  • Oxford Road Boundaries - contains 62605 labelled samples, of which 47639 samples are curated. Each of these samples contain both raw and classified masks for left and right lenses. The data contains images from a diverse set of scenarios such as straight roads, parked cars, and junctions.
  • KITTI Vision Benchmark Suite - 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as highresolution
  • University of Michigan North Campus Long-Term Vision and LIDAR Dataset - consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive
  • University of Michigan Ford Campus Vision and Lidar Data Set - dataset collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS LV) and consumer (Xsens MTI-G) Inertial Measuring Unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system.
  • DIPLECS Autonomous Driving Datasets (2015) - dataset was recorded by placing a HD camera in a car driving around the Surrey countryside. The dataset contains about 30 minutes of driving. The video is 1920x1080 in colour, encoded using H.264 codec. Steering is estimated by tracking markers on the steering wheel. The car's speed is estimated from OCR the car's speedometer (but the accuracy of the method is not guaranteed).
  • Velodyne SLAM Dataset from Karlsruhe Institute of Technology - two challenging datasets recorded with the Velodyne HDL64E-S2 scanner in the city of Karlsruhe, Germany.
  • SYNTHetic collection of Imagery and Annotations (SYNTHIA) - consists of a collection of photo-realistic frames rendered from a virtual city and comes with precise pixel-level semantic annotations for 13 classes: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lanemarking.
  • Cityscape Dataset - focuses on semantic understanding of urban street scenes. large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Details on annotated classes and examples of our annotations are available.
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Self Racing Cars - XSens/Fairchild Dataset - The files include measurements from the Fairchild FIS1100 6 Degree of Freedom (DoF) IMU, the Fairchild FMT-1030 AHRS, the Xsens MTi-3 AHRS, and the Xsens MTi-G-710 GNSS/INS. The files from the event can all be read in the MT Manager software, available as part of the MT Software Suite, available here.
  • Yet Another Computer Vision Index To Datasets (YACVID) - a list of frequently used computer vision datasets.
  • Multisensory Omni-directional Long-term Place Recognition (MOLP) dataset for autonomous driving - directional stereo cameras during one year in Colorado, USA. paper
  • Lane Instance Segmentation in Urban Environments - automated method for labelling lane instances. 24,000 image set available. paper
  • Foggy Zurich Dataset
  • Intel's dataset for AD conditions in India
  • nuScenes Dataset
  • Swedish Traffic Sign Dataset
  • Argoverse Motion Forecasting Dataset
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • Waymo Training and Validation Data
  • University of Michigan Ford Campus Vision and Lidar Data Set - dataset collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS LV) and consumer (Xsens MTI-G) Inertial Measuring Unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system.
  • SYNTHetic collection of Imagery and Annotations (SYNTHIA) - consists of a collection of photo-realistic frames rendered from a virtual city and comes with precise pixel-level semantic annotations for 13 classes: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lanemarking.
  • Daimler Urban Segmetation Dataset - consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames (every 10th frame of the sequence) come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching (sgm).
  • University of Michigan North Campus Long-Term Vision and LIDAR Dataset - consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive
  • Self Racing Cars - XSens/Fairchild Dataset - The files include measurements from the Fairchild FIS1100 6 Degree of Freedom (DoF) IMU, the Fairchild FMT-1030 AHRS, the Xsens MTi-3 AHRS, and the Xsens MTi-G-710 GNSS/INS. The files from the event can all be read in the MT Manager software, available as part of the MT Software Suite, available here.
  • SullyChen AutoPilot Dataset
  • SYNTHetic collection of Imagery and Annotations (SYNTHIA) - consists of a collection of photo-realistic frames rendered from a virtual city and comes with precise pixel-level semantic annotations for 13 classes: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lanemarking.
  • German Traffic Sign Dataset

Foundations

Artificial Intelligence

Computer Vision

  • Awesome Computer Vision - A curated list of awesome computer vision resources, maintained by Jia-Bin Huang
  • Awesome Deep Vision - A curated list of deep learning resources for computer vision, maintained by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim

Robotics

  • Awesome Robotics - A list of various books, courses and other resources for robotics, maintained by kiloreux.

Laws

Twitter

Media

Blogs

Podcasts

Twitter

Open Source Software

Computer Vision

  • Stanford Driving Software - Software Infrastructure for Stanford's Autonomous Vehicles.
  • OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games, mountain car, car racing etc., with a good possibility to develop and validate RL algorithms for Self-Driving Cars.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • Comma.ai Openpilot - an open source driving agent.
  • GTA Robotics SDC Environment - development environment ready for Udacity Self Driving Car (SDC) Challenges.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • argoverse-api - Development kit for working with the Argoverse 3d Tracking and Forecasting datasets, and for evaluating 3d tracking, 3d detection, and motion forecasting algorithms.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • Autoware - Integrated open-source software for urban autonomous driving.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.
  • The OSCC Project - A by-wire control kit for autonomous vehicle development.

Papers

Computer Vision