awesome-autonomous-vehicles

Development kit for working with the

Integrated open-source software for urban autonomous driving.

an open source driving agent.

development environment ready for Udacity Self Driving Car (SDC) Challenges.

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.

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.

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.

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.

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).

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).

Computer Vision, Cognitive Navigation, Spatial Car Environment Capture.

Investigates state-of-the-art technologies in computer vision and machine learning for automotive application.

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.

working in the areas of computer vision, autonomous navigation, virtual reality, intelligent manipulation, space robotics, and related fields.

Computer vision, hardware, and other publications from a UK-based autonomous vehicle company

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).​

A curated list of awesome computer vision resources, maintained by Jia-Bin Huang

A curated list of deep learning resources for computer vision, maintained by Jiwon Kim, Heesoo Myeong, Myungsub Choi, Jung Kwon Lee, Taeksoo Kim

A curated list of awesome Machine Learning frameworks, libraries and software. Maintained by Joseph Misiti.Joseph Misiti

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

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.

Deep Learning curriculum meant to be a starting point for everyone interested in seriously studying the field.

presented by

presented by

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] How to Land An Autonomous Vehicle Job: Coursework

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.

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: