Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
Bai Liu's bachelor thesis project, on optimizing control strategies of auto-driving vehicles at crossroads
Matlab script generates a B-spline path with Parametric Continuity (C2) between two states
Curated List of Self-Driving Cars and Autonomous Vehicles Resources
This repository has additional materials to the paper entitled "Safe Optimization of Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Control"
About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
intelligent and networked control for multi vehicles on intersection
This project explores making a wireless automatic guided robot which does not require any kind of intrusive modifications to be made to the environment, apart from installing an overhead camera. Generally environments that make use of automatic guided vehicles (AGVs) have to plan the path(s) where the robots should go before installing the tracks, like magnetic strips or metal tracks; this is an investment even before using the robots. If any change to the path(s) is required to be made, then more cost is incurred. In this paper a four wheeled differential drive robot has been controlled wirelessly to follow paths drawn on a graphical user interface within a workspace of 1.8m by 1.4m. The robot is controlled by correcting its orientation through visual feedback from a camera. Error analysis was performed to investigate how well the robot followed the path drawn. The estimated error of the robot is within a few centimeters of the path and can be reduced by modifying various thresholds.
A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.
This is the code for "How to Simulate a Self-Driving Car" by Siraj Raval on Youtube
iMorpheus.ai hosts weekly technical journal discussion event through webex. Here is all the papers and slides discussed.
