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https://github.com/manfreddiaz/awesome-autonomous-vehicles

Curated List of Self-Driving Cars and Autonomous Vehicles Resources
https://github.com/manfreddiaz/awesome-autonomous-vehicles
List: awesome-autonomous-vehicles
autonomous-cars autonomous-vehicles car-driving computer-vision deep-learning
Last synced: 20 days ago
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Curated List of Self-Driving Cars and Autonomous Vehicles Resources
Host: GitHub
URL: https://github.com/manfreddiaz/awesome-autonomous-vehicles
Owner: manfreddiaz
Created: 2016-12-09T20:07:33.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2024-03-15T17:19:11.000Z (8 months ago)
Last Synced: 2024-07-31T04:02:03.085Z (3 months ago)
Topics: autonomous-cars, autonomous-vehicles, car-driving, computer-vision, deep-learning
Homepage: https://manfreddiaz.github.io/awesome-autonomous-vehicles/
Size: 281 KB
Stars: 2,149
Watchers: 165
Forks: 581
Open Issues: 1
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome-artificial-intelligence-research - Autonomous Vehicles (did not update since 2016)
awesome-open-transport - AWESOME Autonomous Vehicles Material
ultimate-awesome - awesome-autonomous-vehicles - Curated List of Self-Driving Cars and Autonomous Vehicles Resources. (Other Lists / PowerShell Lists)
README

        # Awesome Autonomous Vehicles: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of awesome autonomous vehicles resources, inspired by [awesome-php](https://github.com/ziadoz/awesome-php).

## Contributing

Please feel free to send me pull requests to add links.

## Table of Contents

* [Foundations](#foundations)

* [Courses](#courses)

* [Papers](#papers)

* [Research Labs](#research-labs)

* [Datasets](#datasets)

* [Open Source Software](#open-source-software)

* [Hardware](#hardware)

* [Toys](#toys)

* [Companies](#companies)

* [Media](#media)

* [Laws](#laws)

## Foundations

### Artificial Intelligence

1. [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) - A curated list of awesome Machine Learning frameworks, libraries and software. Maintained by Joseph Misiti.Joseph Misiti

* [Deep Learning Papers Reading Roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap) - Deep Learning papers reading roadmap constructed from outline to detail, old to state-of-the-art,

from generic to specific areas focus on state-of-the-art for anyone starting in Deep Learning. Maintained by, Flood Sung.

* [Open Source Deep Learning Curriculum](http://www.deeplearningweekly.com/pages/open_source_deep_learning_curriculum) - Deep Learning curriculum  meant to be a starting point for everyone interested in seriously studying the field.

### Robotics

1. [Awesome Robotics](https://github.com/Kiloreux/awesome-robotics) - A list of various books, courses and other resources for robotics, maintained by kiloreux.

### Computer Vision

1. [Awesome Computer Vision](https://github.com/jbhuang0604/awesome-computer-vision) - A curated list of awesome computer vision resources, maintained by Jia-Bin Huang

* [Awesome Deep Vision](https://github.com/kjw0612/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

## Courses

* [[Coursera] Machine Learning](https://www.coursera.org/learn/machine-learning) - presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng), as of 2020 Jan 28 it has 125,344 ratings and 30,705 reviews.

* [[Coursera+DeepLearning.ai]Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) - presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng), 5 Courses, teaches foundations of deep learning, programming language: python

* [[Udacity] Self-Driving Car Nanodegree Program](https://www.udacity.com/course/self-driving-car-engineer-nanodegree--nd013) - teaches the skills and techniques used by self-driving car teams. Program syllabus can be found [here](https://medium.com/self-driving-cars/term-1-in-depth-on-udacitys-self-driving-car-curriculum-ffcf46af0c08#.bfgw9uxd9).

* [[University of Toronto] CSC2541

Visual Perception for Autonomous Driving](http://www.cs.toronto.edu/~urtasun/courses/CSC2541/CSC2541_Winter16.html) - A graduate course in visual perception for autonomous driving. The class briefly covers topics in localization, ego-motion estimaton, free-space estimation, visual recognition (classification, detection, segmentation).

* [[INRIA] Mobile Robots and Autonomous Vehicles](https://www.fun-mooc.fr/courses/inria/41005S02/session02/about?utm_source=mooc-list) - 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] ENG5017 Autonomous Vehicle Guidance Systems](http://www.gla.ac.uk/coursecatalogue/course/?code=ENG5017) - 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] How to Land An Autonomous Vehicle Job: Coursework](https://medium.com/self-driving-cars/how-to-land-an-autonomous-vehicle-job-coursework-e7acc2bfe740#.j5b2kwbso) David Silver, from Udacity, reviews his coursework for landing a job in self-driving cars coming from a Software Engineering background.

* [[Stanford] - CS221 Artificial Intelligence: Principles and Techniques](http://stanford.edu/~cpiech/cs221/index.html) - Contains a simple self-driving project and simulator.

* [[MIT] 6.S094: Deep Learning for Self-Driving Cars](http://selfdrivingcars.mit.edu/) - *"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] Deep Learning](https://deeplearning.mit.edu/) - *"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 Artificial Intelligence](https://hcai.mit.edu/) - *"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] - MAE/ECE148 Introduction to Autonomous Vehicles](https://guitar.ucsd.edu/maeece148/index.php/Introduction_to_Autonomous_Vehicles) - A hands-on, project-based course using DonkeyCar with lane-tracking functionality and various advanced topics such as object detection, navigation, etc.

* [[MIT] 2.166 Duckietown](http://duckietown.mit.edu/index.html) - 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] Self-Driving Cars](https://www.coursera.org/specializations/self-driving-cars#about) - 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.

## Papers

By Topic Areas and Year of Publication / Submission

#### General

1. **[2016]** _Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving_. [[ref](https://arxiv.org/abs/1612.00147)]

* **[2015]** _An Empirical Evaluation of Deep Learning on Highway Driving_. [[ref](https://arxiv.org/abs/1504.01716)]

* **[2015]** _Self-Driving Vehicles: The Challenges and Opportunities Ahead_. [[ref](http://dl.acm.org/citation.cfm?id=2823464)]

* **[2014]** _Making Bertha Drive - An Autonomous Journey on a Historic Route_. [[ref](https://www.semanticscholar.org/paper/Making-Bertha-Drive-An-Autonomous-Journey-on-a-Ziegler-Bender/ec26d7b1cb028749d0d6972279cf4090930989d8)]

* **[2014]** _Towards Autonomous Vehicles_. [[ref](https://www.semanticscholar.org/paper/Towards-Autonomous-Vehicles-Schwarz-Thomas/88712e686e1bcad21f0836e9d31400dab2b7fa8f)]

* **[2013]** _Towards a viable autonomous driving research platform_. [[ref](https://www.semanticscholar.org/paper/Towards-a-viable-autonomous-driving-research-Wei-Snider/da5cee7a6eb817bbbf4721c64c756bd8b7122359)]

* **[2013]** _An ontology-based model to determine the automation level of an automated vehicle for co-driving_. [[ref](https://www.semanticscholar.org/paper/An-ontology-based-model-to-determine-the-Pollard-Morignot/25239ec7fb6159166dfe15adf229fc2415f071df)]

* **[2013]** _Autonomous Vehicle Navigation by Building 3d Map and by Detecting Human Trajectory Using Lidar_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Vehicle-Navigation-by-Building-3d-Map-Kagami-Thompson/81b14341e3e063d819d032b6ce0bc0be0917c867)]

* **[2012]** _Autonomous Ground Vehicles - Concepts and a Path to the Future_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Ground-Vehicles-Concepts-and-a-Path-to-Luettel-Himmelsbach/5e8d51a1f6ba313a38a35af414a00bcfd3b5c0ae)]

* **[2011]** _Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing_. [[ref](https://www.semanticscholar.org/paper/Experimental-Evaluation-of-Autonomous-Driving-Diosi-Segvic/2aeb9aa42e8e2048e15453759ec12411486a2619)]

* **[2011]** _Learning to Drive: Perception for Autonomous Cars_. [[ref](https://www.semanticscholar.org/paper/Learning-to-Drive-Perception-for-Autonomous-Cars-Stavens-Thrun/be25d7bff3b5928adf6c0a7f5495d47113f80997)]

* **[2010]** _Toward robotic cars_. [[ref](http://dl.acm.org/citation.cfm?id=1721679)]

* **[2009]** _Autonomous Driving in Traffic: Boss and the Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Driving-in-Traffic-Boss-and-the-Urban-Urmson-Baker/4657a350e4822bc567256f9b9dc5d922237a71be)]

* **[2009]** _Mapping, navigation, and learning for off-road traversal_. [[ref](https://www.semanticscholar.org/paper/Mapping-navigation-and-learning-for-off-road-Konolige-Agrawal/57d7396b92ad31b386dfce4f8799149f5ced2160)]

* **[2008]** _Autonomous Driving in Urban Environments: Boss and the Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Driving-in-Urban-Environments-Boss-and-Urmson-Anhalt/1c0fb6b1bbfde0f9bab6268f5609cce2bd3bc5bd)]

* **[2008]** _Caroline: An autonomously driving vehicle for urban environments_. [[ref](https://www.semanticscholar.org/paper/Caroline-An-autonomously-driving-vehicle-for-urban-Rauskolb-Berger/08f4e164291942fc78bd6945215b2c672b17edd5)]

* **[2008]** _Design of an Urban Driverless Ground Vehicle_. [[ref](https://www.semanticscholar.org/paper/Design-of-an-Urban-Driverless-Ground-Vehicle-Benenson-Parent/852a672c3d4a2fca3ff7b215d9c096b0be54feb7)]

* **[2008]** _Little Ben: The Ben Franklin Racing Team's Entry in the 2007 DARPA Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Little-Ben-The-Ben-Franklin-Racing-Team-s-Entry-in-Bohren-Foote/b6d5e01cdb76284ee6c42b0dda6c36f121c573f0)]

* **[2008]** _Odin: Team VictorTango's Entry in the DARPA Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Odin-Team-VictorTango-s-Entry-in-the-DARPA-Urban-Reinholtz-Hong/aaeaa58bedf6fa9b42878bf5914f55f48cf26209)]

* **[2008]** _Robosemantics: How Stanley the Volkswagen Represents the World_. [[ref](https://www.semanticscholar.org/paper/Robosemantics-How-Stanley-the-Volkswagen-Parisien-Thagard/9f2186df45a387ab600414968090fe3da37591ca)]

* **[2008]** _Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Team-AnnieWAY-s-Autonomous-System-Stiller-Kammel/56972aa9f9d3cce7c77d402602bc8f3af94d57c9)]

* **[2008]** _The MIT-Cornell collision and why it happened_. [[ref](https://www.semanticscholar.org/paper/The-MIT-Cornell-collision-and-why-it-happened-Fletcher-Teller/0df4f3ef7356fe56547ac3145d7c0229163bc7a5)]

* **[2007]** _Self-Driving Cars - An AI-Robotics Challenge_. [[ref](https://www.semanticscholar.org/paper/Self-Driving-Cars-An-AI-Robotics-Challenge-Thrun/31d17c77d2ea18f71d570741665f0fd3030caa94)]

* **[2007]** _2007 DARPA Urban Challenge: The Ben Franklin Racing Team Team B156 Technical Paper_. [[ref](https://www.semanticscholar.org/paper/2007-Darpa-Urban-Challenge-the-Ben-Franklin-Racing-Franklin-Lee/510b0fa02d6bdd1061cf73373f197ba624692ad0)]

* **[2007]** _Team Mit Urban Challenge Technical Report_. [[ref](https://www.semanticscholar.org/paper/Team-Mit-Urban-Challenge-Technical-Report-Leonard-Barrett/6ac15e819701cd0d077d8157711c4c402106722c)]

* **[2007]** _DARPA Urban Challenge Technical Report Austin Robot Technology_ [[ref](https://www.semanticscholar.org/paper/Darpa-Urban-Challenge-Technical-Report-Executive-Technology-Tuttle/37e78b1bd135df5c5a1fcbf2a8debd260d28a55c)]

* **[2007]** _Spirit of Berlin: an Autonomous Car for the Darpa Urban Challenge Hardware and Software Architecture_. [[ref](https://www.semanticscholar.org/paper/Spirit-of-Berlin-an-Autonomous-Car-for-the-Darpa-Berlin-Rojo/8c96cbc752dfcde3673440cf7ca1fb19218426bf)]

* **[2007]** _Team Case and the 2007 Darpa Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Team-Case-and-the-2007-Darpa-Urban-Challenge-Newman-Lead/e68c745b7807e77ccf67fea325a241136a568eeb)]

* **[2006]** _A Personal Account of the Development of Stanley, the Robot That Won the DARPA Grand Challenge_. [[ref](https://www.semanticscholar.org/paper/A-Personal-Account-of-the-Development-of-Stanley-Thrun/74a4de58be068d2dc38bb31cf54c3c49bdc0d4e4)]

* **[2006]** _Stanley: The robot that won the DARPA Grand Challenge_. [[ref](https://www.semanticscholar.org/paper/Stanley-The-robot-that-won-the-DARPA-Grand-Thrun-Montemerlo/298500897243b17fa2ebe7bde0a1b8ebc00ea07f)]

#### Localization & Mapping

1. **[2016]** _MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System._ [[ref](https://arxiv.org/abs/1610.07336)]

* **[2016]** _Image Based Camera Localization: an Overview_. [[ref](https://arxiv.org/abs/1610.03660)]

* **[2016]** _Ubiquitous real-time geo-spatial localization_ [[ref](http://dl.acm.org/citation.cfm?id=3005426)]

* **[2016]** _Robust multimodal sequence-based loop closure detection via structured sparsity_. [[ref](http://www.roboticsproceedings.org/rss12/p43.pdf)]

* **[2016]** _SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition_. [[ref](http://ieeexplore.ieee.org/document/7839213/)], [[code](https://github.com/hanfeiid/SRAL)]

* **[2015]** _Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras_. [[ref](https://www.semanticscholar.org/paper/Precise-Localization-of-an-Autonomous-Car-Based-on-Jo-Jo/27251099b78185f9ddf59c9ed0c5868af4ef1e80)]

* **[2013]** _Planar Segments Based Three-dimensional Robotic Mapping in Outdoor Environments_. [[ref](https://www.semanticscholar.org/paper/Planar-Segments-Based-Three-dimensional-Robotic-Xiao/ebddeb22f3b5c38422987c3fe51aaf847ad444e7)]

* **[2013]** _Vehicle Localization along a Previously Driven Route Using Image Database_. [[ref](https://www.semanticscholar.org/paper/Vehicle-Localization-along-a-Previously-Driven-Kume-Supp%C3%A9/e5a7ac37d542349ae19281f1e2a571f7030b789c)]

* **[2012]** _Can priors be trusted? Learning to anticipate roadworks_. [[ref](https://www.semanticscholar.org/paper/Can-priors-be-trusted-Learning-to-anticipate-Mathibela-Osborne/0a7e502779ed2cf9ee2677d0310386481a51fc12)]

* **[2009]** _Laser Scanner Based Slam in Real Road and Traffic Environment_. [[ref](https://www.semanticscholar.org/paper/Laser-Scanner-Based-Slam-in-Real-Road-and-Traffic-Garcia-Favrot-Parent/2accb1d9f7ce3f08aa1cde735dcca2578887c545)]

* **[2007]** _Map-Based Precision Vehicle Localization in Urban Environments_. [[ref](https://www.semanticscholar.org/paper/Map-Based-Precision-Vehicle-Localization-in-Urban-Levinson-Montemerlo/924f7268d592d327f97ad4e96f48ad774d982ef3)]

#### Perception

1. **[2019]** _Argoverse: 3D Tracking and Forecasting with Rich Maps_. [[ref](https://arxiv.org/abs/1911.02620))

2. **[2016]** _VisualBackProp: visualizing CNNs for autonomous driving_. [[ref](https://arxiv.org/abs/1611.05418)]

* **[2016]** _Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?_. [[ref](https://arxiv.org/abs/1610.01983)]

* **[2016]** _Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles_. [[ref](https://arxiv.org/abs/1609.04653)]

* **[2016]** _Image segmentation of cross-country scenes captured in IR spectrum_. [[ref](https://arxiv.org/abs/1604.02469)]

* **[2016]** _Traffic-Sign Detection and Classification in the Wild_. [[ref](https://www.semanticscholar.org/paper/Traffic-Sign-Detection-and-Classification-in-the-Zhu-Liang/d463499b7a82e3cad81d2471b52a198b857aa75b)]

* **[2016]** _Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance_. [[ref](https://www.semanticscholar.org/paper/Persistent-self-supervised-learning-principle-from-Hecke-Croon/a48c4c6707fca20ae64b044b6e8f7f37891186fc)]

* **[2016]** _Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion_. [[ref](https://www.semanticscholar.org/paper/Deep-Multispectral-Semantic-Scene-Understanding-of-Valada-Oliveira/8be99dd94bff76c75594a15e114268841a2656a7)]

* **[2016]** _Joint Attention in Autonomous Driving (JAAD)_. [[ref](https://www.semanticscholar.org/paper/Joint-Attention-in-Autonomous-Driving-JAAD--Kotseruba-Rasouli/1e6a26deea0a38310368d9c2a6dadc317b50bdf8), [data](http://data.nvision2.eecs.yorku.ca/JAAD_dataset/)]

* **[2016]** _Perception for driverless vehicles: design and implementation_. [[ref](https://www.semanticscholar.org/paper/Perception-for-driverless-vehicles-design-and-Benenson-Suarez/bf1c728e3e893670244591f720b453245c3363f6)]

* **[2016]** _Robust multimodal sequence-based loop closure detection via structured sparsity_. [[ref](http://www.roboticsproceedings.org/rss12/p43.pdf)]

* **[2016]** _SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition_. [[ref](http://ieeexplore.ieee.org/document/7839213/)], [[code](https://github.com/hanfeiid/SRAL)]

* **[2015]** _Pixel-wise Segmentation of Street with Neural Networks_. [[ref](https://arxiv.org/abs/1511.00513)]

* **[2015]** _Deep convolutional neural networks for pedestrian detection_. [[ref](https://arxiv.org/abs/1510.03608)]

* **[2015]** _Fast Algorithms for Convolutional Neural Networks_. [[ref](https://arxiv.org/abs/1509.09308)]

* **[2015]** _Fusion of color images and LiDAR data for lane classification_. [[ref](http://dl.acm.org/citation.cfm?id=2820859)]

* **[2015]** _Environment Perception for Autonomous Vehicles in Challenging Conditions Using Stereo Vision_. [[ref](https://www.semanticscholar.org/paper/Environment-Perception-for-Autonomous-Vehicles-in-Gal%C3%A1n-Hayet/8f56fd10f37382292f474c441f92432b34b58db5)]

* **[2015]** _Intention-aware online POMDP planning for autonomous driving in a crowd_. [[ref](https://www.semanticscholar.org/paper/Intention-aware-online-POMDP-planning-for-Bai-Cai/481aa2882a5816686a5bea7db755862cded42081)]

* **[2015]** _Survey on Vanishing Point Detection Method for General Road Region Identification_. [[ref](https://www.semanticscholar.org/paper/Survey-on-Vanishing-Point-Detection-Method-for-Patel-Mistry/39c6be1e7723b93f06be2bb4199066d4efdadbc9)]

* **[2015]** _Visual road following using intrinsic images_. [[ref](https://www.semanticscholar.org/paper/Visual-road-following-using-intrinsic-images-Krajn%C3%ADk-Blazicek/2298f9e3c1235526d55cf78bfc80c505d100540f)]

* **[2014]** _Rover – a Lego* Self-driving Car_. [[ref](https://www.semanticscholar.org/paper/Rover-a-Lego-Self-driving-Car-Tan-Wojtczyk-Wojtczyk/6e24123ef558ffb9888d28f992f8afe76622830e)]

* **[2014]** _Classification and Tracking of Dynamic Objects with Multiple Sensors for Autonomous Driving in Urban Environments_. [[ref](https://www.semanticscholar.org/paper/Classification-and-Tracking-of-Dynamic-Objects-Darms-Rybski/6c9ce40060fa3efea7d04a4a0e36609592ed6ddf)]

* **[2014]** _Generating Omni-directional View of Neighboring Objects for Ensuring Safe Urban Driving_. [[ref](https://www.semanticscholar.org/paper/Generating-Omni-directional-View-of-Neighboring-Seo/29e53add392de54d439a6002c67e8af6e9baadeb)]

* **[2014]** _Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Visual-Navigation-and-Laser-Based-Cherubini-Spindler/089fa5a7babc906dc46a58f986c5ac8c46aa9017)]

* **[2014]** _Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation_. [[ref](https://www.semanticscholar.org/paper/Extending-the-Stixel-World-with-online-self-Sanberg-Dubbelman/6dd60e0484931b284f49ab8204b011d153ff4967)]

* **[2014]** _Modeling Human Plan Recognition Using Bayesian Theory of Mind_. [[ref](https://www.semanticscholar.org/paper/Plan-Activity-and-Intent-Recognition-Baker-Tenenbaum/4cbb1ea46c09d11b0b986a7baaac7215006504f8)]

* **[2013]** _Focused Trajectory Planning for autonomous on-road driving_. [[ref](https://www.semanticscholar.org/paper/Focused-Trajectory-Planning-for-autonomous-on-road-Gu-Snider/03bf26d72d8cc0cf401c31e31c242e1894bd0890)]

* **[2013]** _Avoiding moving obstacles during visual navigation_. [[ref](https://www.semanticscholar.org/paper/Avoiding-moving-obstacles-during-visual-navigation-Cherubini-Grechanichenko/7c0e580c0f914086e9c918aef1df561253a71044)]

* **[2013]** _Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition_. [[ref](https://www.semanticscholar.org/paper/Mobile-robot-navigation-system-in-outdoor-Siagian-Chang/7163764c33c3d87c313568c056d50d1bedb25696)]

* **[2013]** _Obstacle detection and mapping in low-cost, low-power multi-robot systems using an Inverted Particle Filter_. [[ref](https://www.semanticscholar.org/paper/Obstacle-detection-and-mapping-in-low-cost-low-Kleppe-Skavhaug/646cc0e592b77d553cc77806e90d99420fb79a8e)]

* **[2013]** _Real-time estimation of drivable image area based on monocular vision_. [[ref](https://www.semanticscholar.org/paper/Real-time-estimation-of-drivable-image-area-based-Neto-Victorino/c50a769c2038e29d9e64077cd4749b6f8d389806)]

* **[2013]** _Road model prediction based unstructured road detection_. [[ref](https://www.semanticscholar.org/paper/Road-model-prediction-based-unstructured-road-Zuo-Yao/b8b2d3da341042d148ed2988216dbb3ddb6081ed)]

* **[2013]** _Selective Combination of Visual and Thermal Imaging for Resilient Localization in Adverse Conditions: Day and Night, Smoke and Fire_. [[ref](https://www.semanticscholar.org/paper/Selective-Combination-of-Visual-and-Thermal-Brunner-Peynot/85b4b1a9780a4bc22f84904a1cfc3eeeb605c9bd)]

* **[2012]** _Road Tracking Method Suitable for Both Unstructured and Structured Roads_. [[ref](https://www.semanticscholar.org/paper/International-Journal-of-Advanced-Robotic-Systems-Proch%C3%A1zka/4819fda4bc778454701f2a4b30db46ec56aa45bc)]

* **[2012]** _Autonomous Navigation and Sign Detector Learning_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Navigation-and-Sign-Detector-Learning-Ellis-Pugeault/0cffe50112452ecdcdaf0d11b33e12cf3c67213e)]

* **[2012]** _Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle_. [[ref](https://www.semanticscholar.org/paper/Design-of-a-Multi-Sensor-Cooperation-Travel-Chen-Li/f5feb2a151c54ec9699924d401a66c193ddd3c8b)]

* **[2012]** _Learning in Reality: a Case Study of Stanley, the Robot That Won the Darpa Challenge_. [[ref](https://www.semanticscholar.org/paper/Learning-in-Reality-a-Case-Study-of-Stanley-the-Glaser-Hennig/01c1f49f5e7f4e7f5d005844aa9443769a2d9306)]

* **[2012]** _Portable and Scalable Vision-Based Vehicular Instrumentation for the Analysis of Driver Intentionality_. [[ref](https://www.semanticscholar.org/paper/Portable-and-Scalable-Vision-Based-Vehicular-Beauchemin-Bauer/c76b5bc64ffd6e13a6c22641b3926a803e5209d5)]

* **[2012]** _What could move? Finding cars, pedestrians and bicyclists in 3D laser data_. [[ref](https://www.semanticscholar.org/paper/What-could-move-Finding-cars-pedestrians-and-Wang-Posner/f56b01df806bc224d5babb71994915df4a08cb44)]

* **[2012]** _The Stixel World_. [[ref](https://www.semanticscholar.org/paper/The-Stixel-World-N-Im/5307f5e2ff2f0403a92b63418ca5812965dcfb90)]

* **[2011]** _Stereo-based road boundary tracking for mobile robot navigation_. [[ref](https://www.semanticscholar.org/paper/Stereo-based-road-boundary-tracking-for-mobile-Chiku-Miura/8bcbb1f13f2ab7f974ba30a0d68aeccf49082759)]

* **[2009]** _Autonomous Information Fusion for Robust Obstacle Localization on a Humanoid Robot_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Information-Fusion-for-Robust-Obstacle-Sridharan-Li/2365b361fb0e5cb801b22900a4c4a421c35ea639)]

* **[2009]** _Learning long-range vision for autonomous off-road driving_. [[ref](https://www.semanticscholar.org/paper/Learning-long-range-vision-for-autonomous-off-road-Hadsell-Sermanet/2d8f527d1a96b0dae209daa6a241cf3255a6ec0d)]

* **[2009]** _On-line road boundary modeling with multiple sensory features, flexible road model, and particle filter_. [[ref](https://www.semanticscholar.org/paper/On-line-road-boundary-modeling-with-multiple-Matsushita-Miura/0fcac22dceb7a7d49a8c792760ae47c500a804d9)]

* **[2008]** _The Area Processing Unit of Caroline - Finding the Way through DARPA's Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/The-Area-Processing-Unit-of-Caroline-Finding-the-Berger-Lipski/4b9db808c06635b784ce6c1409603c0487bcd684)]

* **[2008]** _Vehicle detection and tracking for the Urban Challenge_. [[ref](https://www.semanticscholar.org/paper/Vehicle-detection-and-tracking-for-the-Urban-Darms-Baker/757fbaa9881b9075409a9962819fda64d51307e1)]

* **[2007]** _Low cost sensing for autonomous car driving in highways_. [[ref](https://www.semanticscholar.org/paper/Low-cost-sensing-for-autonomous-car-driving-in-Gon%C3%A7alves-Godinho/b7f302bc8eb37220ba76c2d55325d218a7e03128)]

* **[2007]** _Stereo and Colour Vision Techniques for Autonomous Vehicle Guidance _. [[ref](https://www.semanticscholar.org/paper/Stereo-and-Colour-Vision-Techniques-for-Autonomous-Mark-Proefschrift/97325201f48351df5ef614a01a55f3da818aae0e)]

* **[2000]** _Real-time multiple vehicle detection and tracking from a moving vehicle_. [[ref](https://www.semanticscholar.org/paper/Real-time-multiple-vehicle-detection-and-tracking-Betke-Haritaoglu/864a7068c346ecbc4ef6c4da66e4c8bcc83fe560)]

#### Navigation & Planning

1. **[2016]** _A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware_. [[ref](https://arxiv.org/abs/1611.01235)]

* **[2016]** _End to End Learning for Self-Driving Cars_. [[ref](https://arxiv.org/abs/1604.07316)]

* **[2016]** _A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles_. [[ref](https://arxiv.org/abs/1604.07446)]

* **[2016]** _A Convex Optimization Approach to Smooth Trajectories for Motion Planning with Car-Like Robots_. [[ref](https://www.semanticscholar.org/paper/A-Convex-Optimization-Approach-to-Smooth-Zhu-Schmerling/785b22bbdb04f2ddd4233a4c40d798ed3194374f)]

* **[2016]** _Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms_. [[ref](https://arxiv.org/abs/1603.00939)]

* **[2016]** _Machine Learning for Visual Navigation of Unmanned Ground Vehicles_. [[ref](https://www.semanticscholar.org/paper/Machine-Learning-for-Visual-Navigation-of-Unmanned-Lenskiy-Lee/9b21934ec4f08ed3cd54a7e3a3c7c25b311e1ced)]

* **[2016]** _Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS_. [[ref](https://www.semanticscholar.org/paper/Real-time-self-driving-car-navigation-and-obstacle-Li-Bao/4e8b5a99ae628eea43d7e7410cdfa7f8a2e847d5)]

* **[2016]** _Watch this: Scalable cost-function learning for path planning in urban environments_. [[ref](https://www.semanticscholar.org/paper/Watch-this-Scalable-cost-function-learning-for-Wulfmeier-Wang/d1e51c7e374dca4465a91300e98bfb27335be463)]

* **[2015]** _DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving_. [[ref](https://www.semanticscholar.org/paper/DeepDriving-Learning-Affordance-for-Direct-Chen-Seff/3ba79761192aa4bddd3342db03aa8187516c0fab?citingPapersSort=is-influential&citingPapersLimit=10&citingPapersOffset=0&citedPapersSort=is-influential&citedPapersLimit=10&citedPapersOffset=0), [data](http://deepdriving.cs.princeton.edu/), [code](http://deepdriving.cs.princeton.edu/)]

* **[2015]** _Automatic Driving on Ill-defined Roads: An Adaptive, Shape-constrained, Color-based Method_. [[ref](https://www.semanticscholar.org/paper/Automatic-Driving-on-Ill-defined-Roads-An-Adaptive-Ososinski-Labrosse/36cfe2e94b7b99653e6565642236e0127d43ef5a), [data](http://www.aber.ac.uk/en/cs/research/ir/dss/#road-driving)]

* **[2015]** _A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment_. [[ref](https://www.semanticscholar.org/paper/A-Framework-for-Applying-Point-Clouds-Grabbed-by-Liu-Liang/907189aacae7bff389d6c6592d6e2586dab5168d)]

* **[2015]** _How Much of Driving Is Preattentive?_. [[ref](https://www.semanticscholar.org/paper/How-Much-of-Driving-Is-Preattentive--Pugeault-Bowden/bb9686ea6f154a64fbdc3551fe223da42663baa9)]

* **[2015]** _Map-building and Planning for Autonomous Navigation of a Mobile Robot_. [[ref](https://www.semanticscholar.org/paper/Map-building-and-Planning-for-Autonomous-G%C3%B3mez-Yu/fc5b5b96334d2a0d12ac2d69fa6d46640897f33e)]

* **[2014]** _A Multiple Attribute-based Decision Making model for autonomous vehicle in urban environment_. [[ref](https://www.semanticscholar.org/paper/A-Multiple-Attribute-based-Decision-Making-model-Chen-Zhao/a045d7008e47d4264e06b5d9f509ed505e100084)]

* **[2014]** _A prediction-based reactive driving strategy for highly automated driving function on freeways_. [[ref](https://www.semanticscholar.org/paper/A-prediction-based-reactive-driving-strategy-for-Bahram-Wolf/77d24bd1e83c23bb7cdf59ab06d575a66c03449a)]

* **[2014]** _An RRT-based navigation approach for mobile robots and automated vehicles_. [[ref](https://www.semanticscholar.org/paper/An-RRT-based-navigation-approach-for-mobile-robots-Garrote-Premebida/56cfb13218175d67bf6dc281686c797b8641a3d0)]

* **[2014]** _Image Feature-based Traversability Analysis for Mobile Robot Navigation in Outdoor Environment_. [[ref](https://www.semanticscholar.org/paper/Image-Feature-based-Traversability-Analysis-for-BEKHTI-KOBAYASHI/9fdf6ba484ee59cfac03a6c73e5177a9a70986c5)]

* **[2014]** _Speed Daemon: Experience-Based Mobile Robot Speed Scheduling_. [[ref](https://www.semanticscholar.org/paper/Speed-Daemon-Experience-Based-Mobile-Robot-Speed-Ostafew-Schoellig/9d3c816fb21bfa00d5a86cbb972a4ab7af59dbfb)]

* **[2014]** _Toward human-like motion planning in urban environments_. [[ref](https://www.semanticscholar.org/paper/Toward-human-like-motion-planning-in-urban-Gu-Dolan/30005949ebde80ebe3cd0b96b84a8dcb8b7f919a)]

* **[2013]** _Motion Estimation for Self-Driving Cars with a Generalized Camera_. [[ref](https://www.semanticscholar.org/paper/Motion-Estimation-for-Self-Driving-Cars-with-a-Lee-Fraundorfer/f7f775a4f484706ffbc524accb351cb564469f6a)]

* **[2013]** _Development of a Navigation Control System for an Autonomous Formula Sae-electric Race Car_. [[ref](https://www.semanticscholar.org/paper/Development-of-a-Navigation-Control-System-for-an-Drage/f55796a5f33836017de2cd8023b57efda9882c26)]

* **[2013]** _Low speed automation: Technical feasibility of the driving sharing in urban areas_. [[ref](https://www.semanticscholar.org/paper/Low-speed-automation-Technical-feasibility-of-the-Resende-Pollard/a34161c17343e8f41e200fe5288e2a4aaeafa25a)]

* **[2013]** _Path selection based on local terrain feature for unmanned ground vehicle in unknown rough terrain environment_. [[ref](https://www.semanticscholar.org/paper/Path-selection-based-on-local-terrain-feature-for-Kondo-Sunaga/e58506ef0f6721729d2f72c61e6bb46565b887de)]

* **[2013]** _Stereo-based Autonomous Navigation and Obstacle Avoidance_. [[ref](https://www.semanticscholar.org/paper/Stereo-based-Autonomous-Navigation-and-Obstacle-C%C3%A9sar-Mendes/be6789bd46d16afa45c8962560a56a89a9089355)]

* **[2012]** _Development of an Autonomous Vehicle for High-Speed Navigation and Obstacle Avoidance._ [[ref](https://www.semanticscholar.org/paper/Development-of-an-Autonomous-Vehicle-for-High-Ryu-Ogay/0941bcd18fdf52d9e25984ff067eebe6834ad7c6)]

* **[2012]** _Fast Vanishing-Point Detection in Unstructured Environments_. [[ref](https://www.semanticscholar.org/paper/Fast-Vanishing-Point-Detection-in-Unstructured-Moghadam-Starzyk/c02f52b8b80db037f92facbb605c5715513935fb)]

* **[2012]** _Navigation of an Autonomous Car Using Vector Fields and the Dynamic Window Approach_. [[ref](https://www.semanticscholar.org/paper/Navigation-of-an-Autonomous-Car-Using-Vector-Lima-Augusto/92411ee829021f09cb30186435d888547e00dd0f)]

* **[2012]** _Road direction detection based on vanishing-point tracking_. [[ref](https://www.semanticscholar.org/paper/Road-direction-detection-based-on-vanishing-point-Moghadam-Feng/d2691eb5a030a1b017a944c7fce319ccd4477730)]

* **[2012]** _Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain_. [[ref](https://www.semanticscholar.org/paper/Self-supervised-learning-to-visually-detect-Zhou-Xi/617740b12065ee88049ca9086695ba78ccd3f110)]

* **[2012]** _Visual Navigation for Mobile Robots_. [[ref](https://www.semanticscholar.org/paper/X-Visual-Navigation-for-Mobile-Robots-Andersen-Andersen/7ac3b3fb12f6b071bdc0d8627225efe415c03104)]

* **[2011]** _A new Approach for Robot Motion Planning using Rapidly-exploring Randomized Trees_. [[ref](https://www.semanticscholar.org/paper/A-new-Approach-for-Robot-Motion-Planning-using-Krammer-Granzer/7e084820c195b65e45e9138415f6cac7762f18dc)]

* **[2011]** _Driving me around the bend: Learning to drive from visual gist_. [[ref](https://www.semanticscholar.org/paper/Driving-me-around-the-bend-Learning-to-drive-from-Pugeault-Bowden/2cf7bddfe52d6ca8f5309c3b42d620065126b445)]

* **[2011]** _Optimized route network graph as map reference for autonomous cars operating on German autobahn_. [[ref](https://www.semanticscholar.org/paper/Optimized-route-network-graph-as-map-reference-for-Czerwionka-Wang/644b76b47c88335d40702b3045d4d3743fc13861)]

* **[2011]** _Template-based autonomous navigation and obstacle avoidance in urban environments_. [[ref](https://www.semanticscholar.org/paper/Template-based-autonomous-navigation-and-obstacle-Souza-Sales/65414da8f4a9beaac1df4d5ca0736f474e001096)]

* **[2010]** _Vision-Based Autonomous Navigation System Using ANN and FSM Control_ [[ref](https://www.semanticscholar.org/paper/Vision-Based-Autonomous-Navigation-System-Using-Sales-Shinzato/e1fcccdbc373c9bbd5bd970c34368e7e1aa56424)]

* **[2010]** _An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios_. [[ref](https://www.semanticscholar.org/paper/An-Optimal-control-based-Framework-for-Trajectory-Anderson-Peters/50c955ab0ca25d49204fe0b115669303508b41d0)]

* **[2010]** _Perception for Urban Driverless Vehicles: Design and Implementation_. [[ref](https://www.semanticscholar.org/paper/Perception-for-Urban-Driverless-Vehicles-Design-Benenson-Suarez/0f68760469015de7cf0b21f2b5ed2b0194bb6b81)]

* **[2009]** _Autonomous Offroad Navigation Under Poor GPS Conditions_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Offroad-Navigation-Under-Poor-GPS-Luettel-Himmelsbach/5168a3824d4b90399e16c42f2293c3bf66113d8a)]

* **[2009]** _Autonomous robot navigation in outdoor cluttered pedestrian walkways_. [[ref](https://www.semanticscholar.org/paper/Autonomous-robot-navigation-in-outdoor-cluttered-Saiki-Carballo/7f81a0e925124e9d5738a51fe41c001a908c68f6)]

* **[2009]** _Fast Path Planning in Uncertain Environments: Theory and Experiments_. [[ref](https://www.semanticscholar.org/paper/Fast-Path-Planning-in-Uncertain-Environments-Xu-Kurdila/88228325b82ff3bcd875628c31e34e9018179d3d)]

* **[2009]** _Trajectory Based Autonomous Vehicle following Using a Robotic Driver_. [[ref](https://www.semanticscholar.org/paper/Trajectory-Based-Autonomous-Vehicle-following-Spencer-Jones/f4f6dc62fe8c5fd309f45ebf5240f9c1c1c0b80a)]

* **[2008]** _A Robust Motion Planning Approach for Autonomous Driving in Urban Areas_. [[ref](https://www.semanticscholar.org/paper/A-Robust-Motion-Planning-Approach-for-Autonomous-Fiore-Yoshi/d3660d2f49958841d6d8486467213512772f9aac)]

* **[2008]** _Motion Planning in Urban Environments_. [[ref](https://www.semanticscholar.org/paper/Motion-Planning-in-Urban-Environments-Ferguson-Howard/8fa74131756a50c1562ebf1f03552779803aed67)]

* **[2008]** _Motion planning in urban environments: Part II_. [[ref](https://www.semanticscholar.org/paper/Motion-planning-in-urban-environments-Part-II-Ferguson-Howard/3c33381fa5dfecd02e4f935957831c3d2926bb0f)]

* **[2008]** _Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles_. [[ref](https://www.semanticscholar.org/paper/Planning-Long-Dynamically-Feasible-Maneuvers-for-Likhachev-Ferguson/1f8ca38a1fa455db3388c617697cc91300c59bc6)]

* **[2009]** _Anticipatory Driving for a Robot-Car Based on Supervised Learning_. [[ref](https://www.semanticscholar.org/paper/Anticipatory-Driving-for-a-Robot-Car-Based-on-Markelic-Kulvicius/ee9adb395ed68a2ce4c2a3909dc6d5a0fbf4e0f0)]

* **[2007]** _Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving_.[[ref](https://www.semanticscholar.org/paper/Online-Speed-Adaptation-Using-Supervised-Learning-Stavens-Hoffmann/9db82954df3f4ae829459dcb8719b8a8ed9f4bee)]

* **[2007]** _Predictive Active Steering Control for Autonomous Vehicle Systems_. [[ref](https://www.semanticscholar.org/paper/Predictive-Active-Steering-Control-for-Autonomous-Falcone-Borrelli/abd354d708b98fb60e0d827a41157491289e8d3c)]

* **[2006]** _Probabilistic Terrain Analysis For High-Speed Desert Driving_.[[ref](https://www.semanticscholar.org/paper/Probabilistic-Terrain-Analysis-For-High-Speed-Thrun-Montemerlo/b23a7882b35d0252e5f3011bff15c6dca46ef84e)]

#### Control

1. **[2016]** _Predictive Control for Autonomous Driving with Experimental Evaluation on a Heavy-duty Construction Truck_. [[ref](https://www.semanticscholar.org/paper/Predictive-Control-for-Autonomous-Driving-with-Lima-Se/de87a5d5fbae0733806ba965b2d70fd04596f6e9)]

* **[2015]** _Model Predictive Control of Autonomous Mobility-on-Demand Systems_. [[ref](https://arxiv.org/abs/1509.03985)]

* **[2015]** _Toward integrated motion planning and control using potential fields and torque-based steering actuation for autonomous driving_. [[ref](https://www.semanticscholar.org/paper/Toward-integrated-motion-planning-and-control-Galceran-Eustice/7b2f163eac946fac7351b0861c2b37fb19ffbaa5)]

* **[2013]** _Strategic decision making for automated driving on two-lane, one way roads using model predictive control_. [[ref](https://www.semanticscholar.org/paper/Strategic-decision-making-for-automated-driving-on-Nilsson-Sj%C3%B6berg/0055ca2e60a2ab5cb66c4191d09563dd7f3edd00)]

* **[2012]** _Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge_. [[ref](https://www.semanticscholar.org/paper/Autonomous-vehicles-control-in-the-VisLab-Broggi-Medici/708fdf9bfd3f7d671ced85221012ef27209b92bb)]

* **[2012]** _Optimal Planning and Control for Hazard Avoidance of Front-wheel Steered Ground Vehicles_. [[ref](https://www.semanticscholar.org/paper/Optimal-Planning-and-Control-for-Hazard-Avoidance-Peters/5d5a066547d60a673328cf6db34325910787ba48)]

* **[2009]** _Automatic Steering Methods for Autonomous Automobile Path Tracking_. [[ref](https://www.semanticscholar.org/paper/Automatic-Steering-Methods-for-Autonomous-Snider/18520721525ed81a6ffa6d8b1c7dcbd771e4a64b)]

* **[2009]** _Comparison of Three Control Methods for an Autonomous Vehicle_. [[ref](https://www.semanticscholar.org/paper/Comparison-of-Three-Control-Methods-for-an-Deshpande-Mathur/8fc0580499b0775db60096f52cd2f0ad2c6d24b5)]

#### Simulation

1. **[2016]** _Learning a Driving Simulator_. [[ref](https://arxiv.org/abs/1608.01230)]

* **[2014]** _From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation_. [[ref](https://arxiv.org/abs/1406.7768)]

* **[2014]** _Technical evaluation of the Carolo-Cup 2014 - A competition for self-driving miniature cars_. [[ref](https://www.semanticscholar.org/paper/Technical-evaluation-of-the-Carolo-Cup-2014-A-Zug-Steup/4f57643b95e854bb05fa0c037cbf8898accdbdef)]

* **[2014]** _Crowdsourcing as a methodology to obtain large and varied robotic data sets_. [[ref](https://www.semanticscholar.org/paper/Crowdsourcing-as-a-methodology-to-obtain-large-and-Croon-Gerke/8bdcb90d72eb0494f8f2635dad8ef05a66b8e445)]

* **[2014]** _Efficient Learning of Pre-attentive Steering in a Driving School Framework_. [[ref](https://www.semanticscholar.org/paper/Efficient-Learning-of-Pre-attentive-Steering-in-a-Rudzits-Pugeault/6a65272403a8bb999bc4e86eee3f919e3fbe813d)]

* **[2007]** _A Simulation and Regression Testing Framework for Autonomous Vehicles_. [[ref](https://www.semanticscholar.org/paper/A-Simulation-and-Regression-Testing-Framework-for-Miller-Cenk/c50ef42740ce03e5af9292f9ce1387b83bee8fed)]

* **[2006]** _Robot Competitions Ideal Benchmarks for Robotics Research_. [[ref](https://www.semanticscholar.org/paper/Robot-Competitions-Ideal-Benchmarks-for-Robotics-Behnke/71e5e9e8be8c870b22cadf58338f634ddd856050)]

#### Software Engineering

1. **[2016]** _Evaluation of Sandboxed Software Deployment for Real-time Software on the Example of a Self-Driving Heavy Vehicle_. [[ref](https://arxiv.org/abs/1608.06759)]

* **[2014]** _Engineering the Hardware/Software Interface for Robotic Platforms - A Comparison of Applied Model Checking with Prolog and Alloy_. [[ref](https://arxiv.org/abs/1401.3985)]

* **[2014]** _Comparison of Architectural Design Decisions for Resource-Constrained Self-Driving Cars - A Multiple Case-Study_. [[ref](https://www.semanticscholar.org/paper/Comparison-of-Architectural-Design-Decisions-for-Berger-Dukaczewski/c89f47c93c62c107e6bd75acde89ee7417ebf244)]

* **[2014]** _(Re)liability of Self-driving Cars. An Interesting Challenge!_. [[ref](http://onlinelibrary.wiley.com/doi/10.1002/qre.1707/full)]

* **[2014]** _Explicating, Understanding, and Managing Technical Debt from Self-Driving Miniature Car Projects_. [[ref](http://ieeexplore.ieee.org/document/6974884/)]

* **[2014]** _Towards Continuous Integration for Cyber-Physical Systems on the Example of Self-Driving Miniature Cars_. [[ref](https://www.semanticscholar.org/paper/Towards-Continuous-Integration-for-Cyber-Physical-Berger/2ac2aa0285984f2ce57efa77aab4e372bbc3ee6c)]

* **[2014]** _Saving virtual testing time for CPS by analyzing code coverage on the example of a lane-following algorithm_. [[ref](http://dl.acm.org/citation.cfm?id=2593466)]

* **[2013]** _Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car_[[ref](http://dl.acm.org/citation.cfm?id=2502530)]

* **[2012]** _SAFER: System-level Architecture for Failure Evasion in Real-time Applications_. [[ref](https://www.semanticscholar.org/paper/SAFER-System-level-Architecture-for-Failure-Kim-Bhatia/ff05797dcc041d04f9ed277269916ad6ff92f1f0)]

* **[2011]** _A Flexible Real-Time Control System for Autonomous Vehicles_. [[ref](https://www.semanticscholar.org/paper/A-Flexible-Real-Time-Control-System-for-Autonomous-Meyer-Strobel/f07956d0031ff046c5c719296f7916d7897fdd21)]

* **[2010]** _Automating acceptance tests for sensor- and actuator-based systems on the example of autonomous vehicles_. [[ref](https://www.semanticscholar.org/paper/Automating-acceptance-tests-for-sensor-and-Berger/3bc567143118f8fb34e0460cc3424701683c2511)]

* **[2007]** _Software & Systems Engineering Process and Tools for the Development of Autonomous Driving Intelligence_ [[ref](https://www.semanticscholar.org/paper/Software-Systems-Engineering-Process-and-Tools-for-Basarke-Berger/c564b62cd7df2ed47bb9a6266cc19c83024bc390)]

#### Human-Machine Interaction

1. **[2015]** _User interface considerations to prevent self-driving carsickness_. [[ref](http://dl.acm.org/citation.cfm?id=2809754)]

* **[2014]** _Public Opinion about Self-driving Vehicles_. [[ref](https://www.semanticscholar.org/paper/Public-Opinion-about-Self-driving-Vehicles-Schoettle-Sivak/4984ed8ae3355d58cfad2bd27cb2bc2488cb0e6a)]

* **[2014]** _Setting the Stage for Self-driving Cars: Exploration of Future Autonomous Driving Experiences_. [[ref](https://www.semanticscholar.org/paper/Setting-the-Stage-for-Self-driving-Cars-Pettersson/df428d8015b92902416d07379fb3415a12d64e3f)]

* **[2014]** _Three Decades of Driver Assistance Systems: Review and Future Perspectives_. [[ref](https://www.semanticscholar.org/paper/Three-Decades-of-Driver-Assistance-Systems-Review-Bengler-Dietmayer/2c6d7bcf2ae79b73ad5888f591e159a3d994322b)]

* **[2013]** _Review Article Automotive Technology and Human Factors Research: Past, Present, and Future_. [[ref](https://www.semanticscholar.org/paper/Review-Article-Automotive-Technology-and-Human-Akamatsu-Green/dfe6df56cd5418ce9d6df938858542097157d3e8)]

* **[2012]** _Safe semi-autonomous control with enhanced driver modeling_. [[ref](https://www.semanticscholar.org/paper/Safe-semi-autonomous-control-with-enhanced-driver-Vasudevan-Shia/8e36ebbb6e5409aa911e4121ca37c455ff157218)]

* **[2012]** _Semi-autonomous Car Control Using Brain Computer Interfaces_. [[ref](https://www.semanticscholar.org/paper/Semi-autonomous-Car-Control-Using-Brain-Computer-G%C3%B6hring-Latotzky/e35864047f5b4ac3398ad6f242d2f1407c965f37)]

* **[2011]** _iDriver - Human Machine Interface for Autonomous Cars_. [[ref](https://www.semanticscholar.org/paper/iDriver-Human-Machine-Interface-for-Autonomous-Reuschenbach-Wang/3d7107cdd11af698790736ba5fc9f23cc3f52d04)]

* **[2010]** _Driving an Autonomous Car with Eye Tracking Driving an Autonomous Car with Eye Tracking_. [[ref](https://www.semanticscholar.org/paper/Driving-an-Autonomous-Car-with-Eye-Tracking-Wang-Latotzky/b3aa092b84ae6c9b924ed1a0d9681bbb342249b3)]

* **[2010]** _Remote Controlling an Autonomous Car with an Iphone_. [[ref](https://www.semanticscholar.org/paper/Remote-Controlling-an-Autonomous-Car-with-an-Wang-Ganjineh/a0032e1fbedf61b2a74cfd5f4a9a3edb52689064)]

* **[2009]** _Car-driver Cooperation in Future Vehicles I. Adas and Autonomuos Vehicle_. [[ref](https://www.semanticscholar.org/paper/Car-driver-Cooperation-in-Future-Vehicles-I-Adas-Broggi-Mazzei/c2cc8ad2087d753cc67061d490f966de2c1373a1)]

* **[2009]** _Driver Inattention Detection based on Eye Gaze - Road Event Correlation_. [[ref](https://www.semanticscholar.org/paper/Driver-Inattention-Detection-based-on-Eye-Gaze-Fletcher-Zelinsky/b46f706a9df142f36a58cd7a84c88962f85d93b5)]

#### Infrastructure

1. **[2014]** _Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective_. [[ref](https://arxiv.org/abs/1404.4391)]

* **[2014]** _Priority-based Intersection Control Framework for Self-Driving Vehicles: Agent-based Model Development and Evaluation_. [[ref](https://www.researchgate.net/publication/271738793_Priority-based_Intersection_Control_Framework_for_Self-Driving_Vehicles_Agent-based_Model_Development_and_Evaluation)]

* **[2014]** _A lattice-based approach to multi-robot motion planning for non-holonomic vehicles_. [[ref](https://www.semanticscholar.org/paper/A-lattice-based-approach-to-multi-robot-motion-Cirillo-Uras/74ec451f463c4931c73f35cf327893ac2595e876)]

* **[2005]** _Cooperative autonomous driving: intelligent vehicles sharing city roads_. [[ref](https://www.semanticscholar.org/paper/Cooperative-autonomous-driving-intelligent-Baber-Kolodko/a42f42fa95d8ee6498dff905ed4848437a8f0084)]

* **[2014]** _Achieving Integrated Convoys: Cargo Unmanned Ground Vehicle Development and Experimentation_. [[ref](https://www.semanticscholar.org/paper/Achieving-Integrated-Convoys-Cargo-Unmanned-Ground-Zych-Silver/364ecf6f5af89c7b3e3d11d2269581b420edb003)]

* **[2014]** _Priority-based coordination of mobile robots_. [[ref](https://www.semanticscholar.org/paper/Priority-based-coordination-of-mobile-robots-Gregoire/5fdd722822fe2722d8c90e35461538dbfca10a5e)]

* **[2012]** _Exploration and Mapping with Autonomous Robot Teams Results from the Magic 2010 Competition_. [[ref](https://www.semanticscholar.org/paper/Exploration-and-Mapping-with-Autonomous-Robot-Olson-Strom/9bf0e62b5b2343a0b509a1ac7a658be587a5c37d)]

* **[2012]** _Progress toward multi-robot reconnaissance and the MAGIC 2010 competition_. [[ref](https://www.semanticscholar.org/paper/Progress-toward-multi-robot-reconnaissance-and-the-Olson-Strom/617943baefd909bbf06787fcb8b18b943820c87e)]

#### Law & Society

1. **[2016]** _Autonomous Vehicle Technology: A Guide for Policymakers_. [[ref](https://www.semanticscholar.org/paper/Autonomous-Vehicle-Technology-A-Guide-for-Anderson-Kalra/a0231f6ab2a9feaef92d5481149cdb2142aaeb02)]

* **[2014]** _**WHITE PAPER** Self-driving Vehicles: Current Status of Autonomous Vehicle Development and Minnesota Policy Implications Preliminary White Paper_. [[ref](https://www.semanticscholar.org/paper/Self-driving-Vehicles-Current-Status-of-Autonomous-Lari-Douma/581075c89f6a3945fa43d61aac1329d1e43f9fa3)]

* **[2014]** _Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective_. [[ref](https://www.semanticscholar.org/paper/Are-We-Ready-for-Driver-less-Vehicles-Security-vs-Acharya/ec5b5c434f9d0bfc3954c212226d436e32bcf7d5)]

* **[2014]** _A Survey of Public Opinion about Autonomous and Self-driving_.[[ref](https://www.semanticscholar.org/paper/A-Survey-of-Public-Opinion-about-Autonomous-and-Schoettle-Sivak/5d983c2d2160b9c159b2cdcfcfaded01a4ce2ad6)]

* **[2013]** _Autonomous vehicle social behavior for highway entrance ramp management_. [[ref](https://www.semanticscholar.org/paper/Autonomous-vehicle-social-behavior-for-highway-Wei-Dolan/86482726040d4a924ee339043e4606625a8f64fd)]

## Research Labs

1. 	[Center for Automotive Research at Stanford](https://cars.stanford.edu/) - Current areas of research focuses on human-centered mobility themes like

understanding how people will interact with increasingly automated vehicles, societal impacts of vehicle automation from policy to ethics to law, technology advances in sensing, decision-making and control.

* [SAIL-TOYOTA Center for AI Research at Stanford](http://aicenter.stanford.edu/research/) - The theme of the center is **Human-Centered Artificial Intelligence for Future Intelligent Vehicles and Beyond.**

* [Berkeley DeepDrive](http://bdd.berkeley.edu/) - Investigates state-of-the-art technologies in computer vision and machine learning for automotive application.

* [Princeton Autonomous Vehicle Engineering](http://pave.princeton.edu/) - undergraduate student-led research group at Princeton University dedicated to advancing and promoting the field of robotics through competitive challenges, self-guided research and community outreach.

* [University of Maryland Autonomous Vehicle Laboratory](http://www.avl.umd.edu/) - conducts research and development in the area of biologically inspired design and robotics.

* [University of Waterloo WAVE Laboratory](http://wavelab.uwaterloo.ca/) - Research areas includes Multirotor UAV, Autonomous driving and Multi-Camera Parallel Tracking and Mapping.

* [Oxford Robotics Institute – Autonomous Systems](http://mrg.robots.ox.ac.uk/) - Researches all aspects of land based mobile autonomy.

* [Autonomous Lab - Freie Universität Berlin](http://autonomos-labs.com/) - Computer Vision, Cognitive Navigation, Spatial Car Environment Capture.

* [Honda Research Institute - USA](https://usa.honda-ri.com/home) - engaged in development and integration of multiple sensory modules and the coordination of these components while fulfilling tasks such as stable motion planning,  decision making, obstacle avoidance, and control (test).

* [Toyota-CSAIL Research Center at MIT](http://toyota.csail.mit.edu/) - Aimed at furthering the development of autonomous vehicle technologies, with the goal of reducing traffic casualties and potentially even developing a vehicle incapable of getting into an accident.

* [Princeton Vision & Robotics](http://vision.princeton.edu/research.html) - Autonomous Driving and StreetView.

* [CMU The Robotic Institute Vision and Autonomous Systems Center (VASC)](http://www.ri.cmu.edu/research_center_detail.html?type=aboutcenter&center_id=4&menu_id=262) - working in the areas of computer vision, autonomous navigation, virtual reality, intelligent manipulation, space robotics, and related fields.

* [Five AI](https://five.ai/research) - Computer vision, hardware, and other publications from a UK-based autonomous vehicle company

* [Vehicle Industry Research Center - Széchenyi University](https://jkk-web.sze.hu/?lang=en) - One of the most researched topic is self-driving (a.k.a autonomous) vehicles. The research center is preparing for this new technology-to-come by studying and researching its fundamentals and exploring the possibilities it offers. 

* [Karlsruhe Institute of Technology (KIT)](https://www.kit.edu/topics/mobility.php) - At KIT, about 800 scientists of nearly 40 institutes conduct research into forward-looking, safe, sustainable, and comfortable solutions for future mobility. Scarcity of resources, lacking space, and overstrained infrastructure call for an integrated assessment of transport means and traffic flows. 

## Datasets

1. [Udacity](https://github.com/udacity/self-driving-car/tree/master/datasets) - Udacity driving datasets released for [Udacity Challenges](https://www.udacity.com/self-driving-car). Contains ROSBAG training data. (~80 GB).

* [Comma.ai](https://archive.org/details/comma-dataset) - 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 RobotCar](http://robotcar-dataset.robots.ox.ac.uk/) - over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. The dataset captures many different combinations of weather, traffic and pedestrians, along with longer term changes such as construction and roadworks.

* [Oxford Radar RobotCar](https://oxford-robotics-institute.github.io/radar-robotcar-dataset/) - 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](https://oxford-robotics-institute.github.io/road-boundaries-dataset/) - 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](http://www.cvlibs.net/datasets/kitti/raw_data.php) - 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as highresolution

color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system.

* [University of Michigan North Campus Long-Term Vision and LIDAR Dataset](http://robots.engin.umich.edu/nclt/) -  consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive

sensors for odometry collected using a Segway robot.

* [University of Michigan Ford Campus Vision and Lidar Data Set](http://robots.engin.umich.edu/SoftwareData/Ford) - 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)](http://cvssp.org/data/diplecs/) - 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](http://www.mrt.kit.edu/z/publ/download/velodyneslam/dataset.html) -  two challenging datasets recorded with the Velodyne HDL64E-S2 scanner in the city of Karlsruhe, Germany.

* [SYNTHetic collection of Imagery and Annotations (SYNTHIA)](http://synthia-dataset.net/) - 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](https://www.cityscapes-dataset.com/) - 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.

* [CSSAD Dataset](http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset) - Several real-world stereo datasets exist for the development and testing of algorithms in the fields of perception and navigation of autonomous vehicles. However, none of them was recorded in developing countries and therefore they lack the particular characteristics that can be found in their streets and roads, like abundant potholes, speed bumpers and peculiar flows of pedestrians. This stereo dataset was recorded from a moving vehicle and contains high resolution stereo images which are complemented with orientation and acceleration data obtained from an IMU, GPS data, and data from the car computer.

* [Daimler Urban Segmetation Dataset](http://www.6d-vision.com/scene-labeling) - 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](http://data.selfracingcars.com/) - 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.

* [MIT AGE Lab](http://lexfridman.com/automated-synchronization-of-driving-data-video-audio-telemetry-accelerometer/) - a small sample of the 1,000+ hours of multi-sensor driving datasets collected at AgeLab.

* [Yet Another Computer Vision Index To Datasets (YACVID)](http://yacvid.hayko.at/) -  a list of frequently used computer vision datasets.

* [KUL Belgium Traffic Sign Dataset](http://www.vision.ee.ethz.ch/~timofter/traffic_signs/) - a large dataset with 10000+ traffic sign annotations, thousands of physically distinct traffic signs. 4 video sequences recorded with 8 high resolution cameras mounted on a van, summing more than 3 hours, with traffic sign annotations, camera calibrations and poses. About 16000 background images. The material is captured in Belgium, in urban environments from Flanders region, by GeoAutomation. 

* [LISA: Laboratory for Intelligent & Safe Automobiles, UC San Diego Datasets](http://cvrr.ucsd.edu/LISA/datasets.html) - traffic sign, vehicles detection, traffic lights, trajectory patterns.

* [Multisensory Omni-directional Long-term Place Recognition (MOLP) dataset for autonomous driving](http://hcr.mines.edu/code/MOLP.html) It was recorded using omni-directional stereo cameras during one year in Colorado, USA. [paper](https://arxiv.org/abs/1704.05215)

* [Lane Instance Segmentation in Urban Environments](https://five.ai/datasets) Semi-automated method for labelling lane instances. 24,000 image set available. [paper](https://arxiv.org/pdf/1807.01347.pdf)

* [Foggy Zurich Dataset](https://www.vision.ee.ethz.ch/~csakarid/Model_adaptation_SFSU_dense/) Curriculum Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding. 3.8k High Quality Foggy images in and around Zurich. [paper](https://arxiv.org/abs/1901.01415)

* [SullyChen AutoPilot Dataset](https://github.com/SullyChen/Autopilot-TensorFlow) Dataset collected by SullyChen in and around California. 

* [Waymo Training and Validation Data](https://waymo.com/open) One terabyte of data with 3D and 2D labels.

* [Intel's dataset for AD conditions in India](https://idd.insaan.iiit.ac.in/) A dataset for Autonomous Driving conditions in India (road scene understanding in unstructured environments) which consists of 10k images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads (by Intel & IIIT Hyderabad).

* [nuScenes Dataset](https://www.nuscenes.org/) A large dataset with 1,400,000 images and 390,000 lidar sweeps from Boston and Singapore. Provides manually generated 3D bounding boxes for 23 object classes.

* [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset) A large dataset of German traffic sign recogniton data (GTSRB) with more than 40 classes in 50k images and detection data (GTSDB) with 900 image annotations.

* [Swedish Traffic Sign Dataset](https://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/) A dataset with traffic signs recorded on 350 km of Swedish roads, consisting of 20k+ images with 20% of annotations.

* [Argoverse 3d Tracking Dataset](https://www.argoverse.org/) A large dataset with ~1M images and ~1M labeled 3d cuboids from Miami and Pittsburgh. Provides HD maps and imagery from 7 ring cameras, 2 stereo cameras, and LiDAR.

* [Argoverse Motion Forecasting Dataset](https://www.argoverse.org/) A large dataset with trajectories of tracked objects across 324,557 scenes, mined from 1006 hours of driving.

## Open Source Software

1. [Autoware](https://github.com/CPFL/Autoware) - Integrated open-source software for urban autonomous driving.

* [Comma.ai Openpilot](https://github.com/commaai/openpilot) - an open source driving agent.

* [Stanford Driving Software](https://sourceforge.net/projects/stanforddriving/) - Software Infrastructure for Stanford's Autonomous Vehicles.

* [GTA Robotics SDC Environment](https://github.com/OSSDC/self-driving-car-1) - development environment ready for Udacity Self Driving Car (SDC) Challenges.

* [The OSCC Project](http://oscc.io/) - A by-wire control kit for autonomous vehicle development.

* [OpenAI Gym](https://gym.openai.com/) - 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.

* [argoverse-api](https://github.com/argoai/argoverse-api) - Development kit for working with the [Argoverse](https://www.argoverse.org/) 3d Tracking and Forecasting datasets, and for evaluating 3d tracking, 3d detection, and motion forecasting algorithms.

## Hardware

## Toys

1. [TensorKart](https://github.com/kevinhughes27/TensorKart) - self-driving MarioKart with TensorFlow.

2. [NeuroJS](https://github.com/janhuenermann/neurojs) - A javascript deep learning and reinforcement learning library. A sample self-driving car implementation.

3. [DonkeyCar](https://github.com/autorope/donkeycar) - A minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.

## Companies

1. (As of August 28, 2019) [40+ Corporations Working On Autonomous Vehicles](https://www.cbinsights.com/blog/autonomous-driverless-vehicles-corporations-list/)

## Media

Different media sources where we can find self-driving car related topics, ideas, and much more.

### Podcasts

* [Artificial Intelligence: AI Podcast](https://lexfridman.com/ai/) - *"Artificial Intelligence podcast (AI podcast) is a series of conversations about technology, science, and the human condition hosted by Lex Fridman."*. Example episodes:

  * [Sebastian Thrun: Flying Cars, Autonomous Vehicles, and Education](https://www.youtube.com/watch?v=ZPPAOakITeQ&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)

  * [Elon Musk: Neuralink, AI, Autopilot, and the Pale Blue Dot](https://www.youtube.com/watch?v=smK9dgdTl40&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4)

  * [George Hotz: Comma.ai, OpenPilot, and Autonomous Vehicles](https://lexfridman.com/george-hotz/)

  * [Jeremy Howard: fast.ai Deep Learning Courses and Research](https://lexfridman.com/jeremy-howard/)

* [Autonocast, The future of transportation](https://www.autonocast.com/) - 

*"A weekly show discussing the latest in transportation technology"*

#### Youtube

1. [Lex Fridman (channel)](https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA) - 100+ of AI and autonomous driving related videos including [MIT Deep Learning Series (playlist)](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) which includes:

   * [11 Jan 2020] [Deep Learning State of the Art (2020)](https://www.youtube.com/watch?v=0VH1Lim8gL8&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&index=1), and

   * [12 Jan 2019] [MIT Deep Learning Basics: Introduction and Overview](https://www.youtube.com/watch?v=O5xeyoRL95U&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&index=2).

1. The Three Pillars of Autonomous Driving. [[watch](https://www.youtube.com/watch?v=GZa9SlMHhQc)]

1. What goes into sensing for autonomous driving? [[watch](https://www.youtube.com/watch?v=GCMXXXmxG-I)]

1. Amnon Shashua CVPR 2016 keynote: Autonomous Driving, Computer Vision and Machine Learning. [[watch](https://www.youtube.com/watch?v=n8T7A3wqH3Q)]

1. Chris Urmson: How a driverless car sees the road. [[watch](https://www.youtube.com/watch?v=tiwVMrTLUWg)]

1. Deep Reinforcement Learning for Driving Policy. [[watch](https://www.youtube.com/watch?v=cYTVXfIH0MU)]

1. NVIDIA at CES 2016 - Self Driving Cars and Deep Learning GPUs. [[watch](https://www.youtube.com/watch?v=KkpxA5rXjmA)]

1. NVIDIA Drive PX2 self-driving car platform visualized. [[watch](https://www.youtube.com/watch?v=URmxzxYlmtg&app=desktop)]

### Blogs

1. [Deep Learning and Autonomous Driving](https://handong1587.github.io/deep_learning/2015/10/09/dl-and-autonomous-driving.html)

* [[Medium] Self-Driving Cars](https://medium.com/self-driving-cars)

### Twitter

1. [comma.ai](https://twitter.com/comma_ai)

* [[Udacity] David Silver](https://twitter.com/dsilver829)

* [[Udacity] Dhruv Parthasarathy](https://twitter.com/dhruvp)

* [[Udacity] Eric Gonzalez](https://twitter.com/ericrgon)

* [[Udacity] Oliver Cameron](https://twitter.com/olivercameron)

* [[Udacity] MacCallister Higgins](https://twitter.com/macjshiggins)

* [[Udacity] Sebastian Thrun](https://twitter.com/SebastianThrun)

* [[Google] Chris Urmson](https://twitter.com/chris_urmson)

## Laws

United States

1. [California Regulatory Notice](https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/testing)

* [Michigan Just Passed the Most Permissive Self-Driving Car Laws in the Country](http://fortune.com/2016/12/09/michigan-self-driving-cars/)

* [Car accidents involving a SDC in California](https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/autonomousveh_ol316)

* [Nvidia starts testing its self-driving cars on public roads](http://www.theinquirer.net/inquirer/news/2479432/nvidia-starts-testing-its-self-driving-cars-on-public-roads)