Here are Resources That I have followed to get understannding about
A) Autonomous vehicles
Introductory:
1.Self-Driving Fundamentals: Featuring Apollo - https://www.udacity.com/course/self-driving-car-fundamentals-featuring-apollo--ud0419
2.YouTube Lecture's held by Lex Fridman at MIT on self driving cars -
Links to individual lecture videos for the course:
Lecture 1: Introduction to Deep Learning and Self-Driving Cars https://youtu.be/1L0TKZQcUtA
Lecture 2: Deep Reinforcement Learning for Motion Planning https://youtu.be/QDzM8r3WgBw
Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task https://youtu.be/U1toUkZw6VI
Lecture 4: Recurrent Neural Networks for Steering through Time https://youtu.be/nFTQ7kHQWtc
Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles https://youtu.be/ByZF8_-OJNI
Industrial Lecture series: https://www.youtube.com/playlist?list=PLrAXtmErZgOeY0lkVCIVafdGFOTi45amq
3.YouTube Lecture's held by Cyrill stachniss and colleagues at University of Bonn on self driving cars - https://www.youtube.com/playlist?list=PLgnQpQtFTOGQo2Z_ogbonywTg8jxCI9pD
4.Self driving cars specilization by University of Toronto- https://www.coursera.org/specializations/self-driving-cars?action=enroll&fbclid=IwAR1Q7ejN_fGVSFQlhxmipMWKh8N7ZU2Qq8d7ODu_UXlIX_HSjKpf80laYtM&utm_campaign=B2C_RL_self-driving-cars_computer-science_software-developement_prospecting%20-&utm_content=B2C_RL_self-driving-cars_long-copy_job-demand_video_15-sec_mid-logo&utm_medium=onlineads&utm_source=fb&utm_term=B2C_RL_self-driving-cars_International_lookalike_1%25
5.Self Driving Car Engineer Nanodegree program by Udacity- https://classroom.udacity.com/nanodegrees/nd013/parts/168c60f1-cc92-450a-a91b-e427c326e6a7/locked
6.Emerging Automotive technologies by Chalmers university of technology on Edx- https://www.edx.org/micromasters/chalmersx-emerging-automotive-technologies
Intermediate: 1.Autoware coursework on self driving cars- https://www.autoware.org/awf-course
B) Deep learning & Reinforcement learning
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Deep learrning specialization by Andrew Ng on coursera- https://www.coursera.org/specializations/deep-learning
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Machine learning specialization on coursera- https://www.coursera.org/learn/machine-learning
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Reinforcement learning Specialization on coursera- https://www.coursera.org/specializations/reinforcement-learning
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Reinforcement learning resources by david silver- https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver
5.Advanced Deep Learning & Reinforcement Learning lecture playlist by Deep mind at UCB- https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
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Reinforcement learning onramp by Mathworks- https://matlabacademy.mathworks.com/R2020b/portal.html?course=reinforcementlearning
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Model predictive RL- https://www.youtube.com/watch?v=X2s7gy3wIYw&feature=youtu.be
C) Perception: Lectures on Perception for Autonomous vehicle held at TUM:
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Introduction to deep learning- https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e
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Advanced Deep learning for computer vision- https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39
3.Object Detection, Tracking & segmentation- https://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs
- Image segmentation: https://github.com/Attila94/refinenet-keras
D) Sensor Fusion & Localization: 1.Lectures on Photogrammetry & SLAM by cyrill stachniss held at University of Bonn- Photogrammetry 1- https://www.youtube.com/playlist?list=PLgnQpQtFTOGTPQhKBOGgjTgX-mzpsOGOX
Photogrammetry 2-https://www.youtube.com/playlist?list=PLgnQpQtFTOGQEXN2Qo571uvwIGNGAM8uf
SLAM- https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_
2.Sensor Fusion Toolbox by MathWork- https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
https://www.mathworks.com/help/pdf_doc/fusion/fusion_ref.pdf
3.Videos:
Understanding Sensor Fusion and Tracking- https://www.mathworks.com/videos/series/understanding-sensor-fusion-and-tracking.html
E) Control: 1.MPC Lab @ UC-Berkeley- http://www.mpc.berkeley.edu/mpc-course-material Imp Topics: 1. Longitudinal Model development
Lateral Model development
Analysis of Model for control
Longitudinal Control - PID, MPC
Adaptive Cruise Control
Lateral Control - PID, MPC
Lane Keeping Control, Lane Change Control
Unified Long/Lat Model development (higher-order model)
MPC Constraints and control
Reinforcement Learning Control and Examples
Analyze principles of operation of a variety of mixed domain actuation systems, including electromechanical and fluid power systems.
Develop mathematical models of a variety of mixed domain actuation systems, and develop familiarity with actuator selection procedures.
Formulate and manipulate control system block diagrams from component and subsystem models;
develop familiarity with control system performance specifications and relation to plant characteristics.
Use graphical and analytical approaches for control system analysis.
Feedback Control System - PID"
-Formulation of optimal control problems
Parameter optimization versus path optimization
Local and global optima; general conditions on existence and uniquenes.
Some basic facts from finite-dimensional optimization.
The Calculus of Variations
The Minimum (Maximum) Principle and the Hamilton-Jacobi Theory
Pontryagin's minimum principle
Optimal control with state and control constraints
Time-optimal control
Singular solutions
Hamilton-Jacobi-Bellman (HJB) equation, and dynamical programming
Viscosity solutions to HJB
Linear Quadratic Gaussian (LQG) Problems
Finite-time and infinite-time state (or output) regulators
Riccati equation and its properties
Tracking and disturbance rejection
Kalman filter and duality
The LQG design
Nonholonomic System Optimal Control"
F)Robotics & Robot Operating System-
1.Mobole Robotics & sensing: a.https://www.youtube.com/playlist?list=PLgnQpQtFTOGSeTU35ojkOdsscnenP2Cqx
b.Mobile Sensing and Robotics 1 & 2(Summer 2019 & 2020, Uni Bonn)-
https://www.youtube.com/playlist?list=PLgnQpQtFTOGQJXx-x0t23RmRbjp_yMb4v
https://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6
2.ROS: https://www.youtube.com/playlist?list=PLK0b4e05LnzZWg_7QrIQWyvSPX2WN2ncc
https://www.youtube.com/playlist?list=PLRG6WP3c31_U7TFGduEIJWVtkOw6AJjFf
https://www.youtube.com/playlist?list=PLE-BQwvVGf8HOvwXPgtDfWoxd4Cc6ghiP
ROS (Robotic OS) 👉 ROS tutorials are a great help if you want to learn about the OS behind self-driving cars for free. You will work with command lines, and learn to build concrete projects.
Artificial Intelligence 👉 I highly recommend Artificial Intelligence for Robotics by Udacity for a practical free introduction to self-driving cars.
G) Advanced C++ for computer vision,linux Cmake(build), git(Version control), gtest, gitlab(Project Management): University of Bonn:
https://www.youtube.com/playlist?list=PLgnQpQtFTOGRM59sr3nSL8BmeMZR9GCIA
https://www.youtube.com/playlist?list=PLgnQpQtFTOGR50iIOtO36nK6aNPtVq98C
C++ - The Main Language 👉 C++ Nanodegree, Udacity Read my C++ interview with Udacity to launch this course here.
👉 Beginning C++ Programming - From beginner to beyond, Udemy Course
Python - Useful to have You shouldn't spend too much time on it, especially if you already have programming skills. 👉 Complete Python Bootcamp: Go from Zero to hero in Python 3 👉 Python Programming Masterclass
Basic Linux Command Lines 👉 I'd say it's not necessary to take a course on this, learn while doing your projects and force yourself to use a Linux system.
These were languages, the other very important thing is maths. Intermediate Probability Intermediate Calculus Intermediate Linear Algebra 👉 You can learn these on Khan Academy