awesome-self-driving-car
An awesome list of self-driving cars
https://github.com/daohu527/awesome-self-driving-car
Last synced: 4 days ago
JSON representation
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Opensource
- openpilot - Openpilot is an open source driver agent. Use the iphone to control the car, which provides adaptive cruise control (ACC) and lane keeping assist (LKAS).
- OpenCV library - OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.
- TensorFlow - TensorFlow is an open source software library for numerical computation using data flow graphs. Used for automatic driving perception and prediction.
- ompl - The Open Motion Planning Library.
- apollo - Apollo is an open source autopilot platform that contains almost everything. Including hardware, systems, vehicle platforms, cloud services, etc. You can quickly build a self-driving system of your own by Apollo.
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Courses
- MIT 6.S094: Deep Learning for Self-Driving Cars - 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. By the way, it's *free*!
- BitTiger Build Your Own Autonomous Vehicle Mastery Program - Two weeks of live classes in Bay Area taught by engineers from [Vector.ai](http://vectorai.io/) on building a self-driving mini car from ground up. Topics include deep learning, ROS, sensors, computer vision, localization, mapping and control. The program costs $7,000.
- Apollo Autopilot Introduction - Note: it's a Chinese tutorial. This is an open class between Baidu and Peking University. It is very comprehensive and detailed, which is a good introductory course.
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Papers & Blogs
- Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys - 2023
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies - 2020
- A Survey of Deep Learning Techniques for Autonomous Driving - 2020
- Self-Driving Cars: A Survey - 2019
- Towards Fully Autonomous Driving: Systems and Algorithms - 2011
- A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications - JIOT 2017
- Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes - ICRA 2018
- Map-Based Precision Vehicle Localization in Urban Environments
- Robust Vehicle Localization in Urban Environments Using Probabilistic Maps
- Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art - CVPR 2017
- Object Detection in 20 Years: A Survey - CVPR 2019
- 3D Object Detection from Images for Autonomous Driving: A Survey - 2022
- A survey on deep learning based methods and datasets for monocular 3D object detection - 2021
- Object Detection With Deep Learning: A Review - CVPR 2018
- Deep Learning for Generic Object Detection: A Survey - CVPR 2018
- 50 Years of object recognition: Directions forward - 2013
- 3D Object Detection for Autonomous Driving: A Survey - 2022
- 3D Object Detection for Autonomous Driving: A Comprehensive Survey - 2022
- A survey on 3d object detection methods for autonomous driving applications - 2019
- Deep Learning-based Image 3D Object Detection for Autonomous Driving - 2023
- Monocular 3d object detection for autonomous driving - 2016
- A survey of robust 3d object detection methods in point clouds - 2022
- Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy - 2021
- Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges - 2020
- Multi-modal 3d object detection in autonomous driving: a survey - 2021
- A review and comparative study on probabilistic object detection in autonomous driving - 2021
- Deep Learning in Video Multi-Object Tracking: A Survey - Neurocomputing 2019
- Deep Learning for Multi-Object Tracking: A Survey - 2019
- Recent progress in road and lane detection: a survey - 2014
- A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving - LG 2019
- Annual Review of Control, Robotics, and Autonomous Systems - 2018
- A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles - Robotics 2016
- A Review of Motion Planning Techniques for Automated Vehicles - 2016
- ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst - Waymo's paper about how to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
- Baidu Apollo EM Motion Planner - A real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
- End to End Learning for Self-Driving Cars - 2016 NVIDIA
- An Introduction to LIDAR - Awesome introduction by [Voyage](http://voyage.auto/) about the key sensor of self-driving cars.
- Learning a Driving Simulator - [comma.ai](http://comma.ai/)'s approach for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper investigates variational autoencoders with classical and learned cost functions using generative adversarial networks for embedding road frames. A transition model is learned in the embedded space using action conditioned Recurrent Neural Networks (RNNs).
- The Third Transportation Revolution - Awesome blog post by [John Zimmer](https://twitter.com/johnzimmer) on [Lyft](https://www.lyft.com/)'s vision for self-driving cars. *Spoiler alert*, John predicts self-driving cars will account for the majority of Lyft rides within 5 years. And by 2025, private car ownership will all-but end in major U.S. cities.
- Self-Driving Cars: A Survey - 2019
- Multiple Object Tracking: A Literature Review - CVPR 2014
- Recent progress in road and lane detection: a survey - 2014
- A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving - LG 2019
- An Introduction to LIDAR - Awesome introduction by [Voyage](http://voyage.auto/) about the key sensor of self-driving cars.
- Recent progress in road and lane detection: a survey - 2014
- An Introduction to LIDAR - Awesome introduction by [Voyage](http://voyage.auto/) about the key sensor of self-driving cars.
- A review and comparative study on probabilistic object detection in autonomous driving - 2021
- Recent progress in road and lane detection: a survey - 2014
- An Introduction to LIDAR - Awesome introduction by [Voyage](http://voyage.auto/) about the key sensor of self-driving cars.
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Datasets and Benchmarks
- Cityscapes - Semantic, instance-wise, dense pixel annotations of 30 classes.
- Udacity's Driving Dataset [Videos - Eight hours (over 280 GB) of driving data collected for their [open source self-driving car challenges](https://www.udacity.com/self-driving-car). Udacity also provides convenient [scripts](https://github.com/rwightman/udacity-driving-reader) to port the data.
- Washington DC's Lidar Data - Lidar point cloud of the entire Washington DC area is made available by the District of Columbia’s Office of the Chief Technology Officer (OCTO).
- nuScenes - The nuScenes dataset (pronounced /nuːsiːnz/) is a public large-scale dataset for autonomous driving provided by nuTonomy-Aptiv.
- Swedish Traffic Sign - A dataset with traffic signs recorded on 350 km of Swedish roads, consisting of 20 000 images with 20% of annotations.
- LISA - 7855 annotations on 6610 frames.
- Tsinghua-Tencent 100K - 100000 images containing 30000 traffic-sign instances.
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Cloud service
- Lgsvl - The LGSVL Simulator is a simulator that facilitates testing and development of autonomous driving software systems. The LGSVL simulator is developed by the Advanced Platform Lab at the LG Electronics America R&D Center, formerly the LG Silicon Valley Lab.
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Big Players
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