{"id":13762191,"url":"https://github.com/Mayakshanesht/Autonomous_Driving_Lecture_resources","last_synced_at":"2025-05-10T14:31:29.037Z","repository":{"id":49152535,"uuid":"334598064","full_name":"Mayakshanesht/Autonomous_Driving_Lecture_resources","owner":"Mayakshanesht","description":null,"archived":false,"fork":false,"pushed_at":"2022-10-18T09:36:56.000Z","size":9078,"stargazers_count":25,"open_issues_count":0,"forks_count":6,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-16T20:31:23.449Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":["Courses"],"sub_categories":[],"readme":"# Autonomous_Driving_Lecture_resources\nHere are Resources That I have followed to get understannding about \n\nA) Autonomous vehicles\n\nIntroductory:\n\n1.Self-Driving Fundamentals: Featuring Apollo -\nhttps://www.udacity.com/course/self-driving-car-fundamentals-featuring-apollo--ud0419\n\n2.YouTube Lecture's held by Lex Fridman at MIT on self driving cars -\n\nLinks to individual lecture videos for the course:\n\nLecture 1: Introduction to Deep Learning and Self-Driving Cars\nhttps://youtu.be/1L0TKZQcUtA​\n\nLecture 2: Deep Reinforcement Learning for Motion Planning\nhttps://youtu.be/QDzM8r3WgBw​\n\nLecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task \nhttps://youtu.be/U1toUkZw6VI​\n\nLecture 4: Recurrent Neural Networks for Steering through Time \nhttps://youtu.be/nFTQ7kHQWtc​\n\nLecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles \nhttps://youtu.be/ByZF8_-OJNI\n\nIndustrial Lecture series:\nhttps://www.youtube.com/playlist?list=PLrAXtmErZgOeY0lkVCIVafdGFOTi45amq\n\n3.YouTube Lecture's held by Cyrill stachniss and colleagues at University of Bonn on self driving cars -\nhttps://www.youtube.com/playlist?list=PLgnQpQtFTOGQo2Z_ogbonywTg8jxCI9pD\n\n4.Self driving cars specilization by University of Toronto-\nhttps://www.coursera.org/specializations/self-driving-cars?action=enroll\u0026fbclid=IwAR1Q7ejN_fGVSFQlhxmipMWKh8N7ZU2Qq8d7ODu_UXlIX_HSjKpf80laYtM\u0026utm_campaign=B2C_RL_self-driving-cars_computer-science_software-developement_prospecting%20-\u0026utm_content=B2C_RL_self-driving-cars_long-copy_job-demand_video_15-sec_mid-logo\u0026utm_medium=onlineads\u0026utm_source=fb\u0026utm_term=B2C_RL_self-driving-cars_International_lookalike_1%25 \n\n5.Self Driving Car Engineer Nanodegree program by Udacity-\nhttps://classroom.udacity.com/nanodegrees/nd013/parts/168c60f1-cc92-450a-a91b-e427c326e6a7/locked\n\n6.Emerging Automotive technologies by Chalmers university of technology on Edx- \nhttps://www.edx.org/micromasters/chalmersx-emerging-automotive-technologies\n\nIntermediate:\n1.Autoware coursework on self driving cars-\nhttps://www.autoware.org/awf-course\n\nB) Deep learning \u0026 Reinforcement learning\n1. Deep learrning specialization by Andrew Ng on coursera-\nhttps://www.coursera.org/specializations/deep-learning\n\n2. Machine learning specialization on coursera-\nhttps://www.coursera.org/learn/machine-learning\n\n3. Reinforcement learning Specialization on coursera-\nhttps://www.coursera.org/specializations/reinforcement-learning\n\n4. Reinforcement learning resources by david silver-\nhttps://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver\n\n5.Advanced Deep Learning \u0026 Reinforcement Learning lecture playlist by Deep mind at UCB-\nhttps://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs\n\n6. Reinforcement learning onramp by Mathworks-\nhttps://matlabacademy.mathworks.com/R2020b/portal.html?course=reinforcementlearning\n\n7. Model predictive RL-\nhttps://www.youtube.com/watch?v=X2s7gy3wIYw\u0026feature=youtu.be\n\nC) Perception: \nLectures on Perception for Autonomous vehicle held at TUM:\n1. Introduction to deep learning-\nhttps://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e\n\n2. Advanced Deep learning for computer vision-\nhttps://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39\n\n3.Object Detection, Tracking \u0026 segmentation-\nhttps://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs\n\n4. Image segmentation:\nhttps://github.com/Attila94/refinenet-keras\n\nD) Sensor Fusion \u0026 Localization:\n1.Lectures on Photogrammetry \u0026 SLAM by cyrill stachniss held at University of Bonn-\nPhotogrammetry 1- https://www.youtube.com/playlist?list=PLgnQpQtFTOGTPQhKBOGgjTgX-mzpsOGOX\n\nPhotogrammetry 2-https://www.youtube.com/playlist?list=PLgnQpQtFTOGQEXN2Qo571uvwIGNGAM8uf\n\nSLAM- https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_\n\n2.Sensor Fusion Toolbox by MathWork- \nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n\nhttps://www.mathworks.com/help/pdf_doc/fusion/fusion_ref.pdf\n\nhttps://in.mathworks.com/content/dam/mathworks/ebook/gated/sensor-fusion-and-tracking-autonomous-systems-ebook.pdf\n\nhttps://www.mathworks.com/campaigns/offers/sensor-fusion-tracking-for-autonomous-systems.confirmation.html?elqsid=1612079905788\u0026potential_use=Student\n\nhttps://www.mathworks.com/content/dam/mathworks/fact-sheet/sensor-fusion-cheat-sheet-quick-start-guide.pdf\n\n3.Videos:\n\nUnderstanding Sensor Fusion and Tracking- https://www.mathworks.com/videos/series/understanding-sensor-fusion-and-tracking.html\n\nE) Control:\n1.MPC Lab @ UC-Berkeley-\nhttp://www.mpc.berkeley.edu/mpc-course-material\nImp Topics:\n1. \nLongitudinal Model development\n\nLateral Model development\n\nAnalysis of Model for control\n\nLongitudinal Control - PID, MPC\n\nAdaptive Cruise Control\n\nLateral Control - PID, MPC\n\nLane Keeping Control, Lane Change Control\n\nUnified Long/Lat Model development (higher-order model)\n\nMPC Constraints and control\n\nReinforcement Learning Control and Examples\n\n2.\nAnalyze principles of operation of a variety of mixed domain actuation systems,\nincluding electromechanical and fluid power systems.\n\nDevelop mathematical models of a variety of mixed domain actuation systems, and develop familiarity with actuator selection procedures.\n\nFormulate and manipulate control system block diagrams from component and\nsubsystem models;\n\ndevelop familiarity with control system performance specifications and relation to plant characteristics.\n\nUse graphical and analytical approaches for control system analysis.\n\nFeedback Control System - PID\"\n\n3.\n-Formulation of optimal control problems\n\nParameter optimization versus path optimization\n\nLocal and global optima; general conditions on existence and uniquenes.\n\nSome basic facts from finite-dimensional optimization.\n\nThe Calculus of Variations\n\nThe Minimum (Maximum) Principle and the Hamilton-Jacobi Theory\n\nPontryagin's minimum principle\n\nOptimal control with state and control constraints\n\nTime-optimal control\n\nSingular solutions\n\nHamilton-Jacobi-Bellman (HJB) equation, and dynamical programming\n\nViscosity solutions to HJB\n\nLinear Quadratic Gaussian (LQG) Problems\n\nFinite-time and infinite-time state (or output) regulators\n\nRiccati equation and its properties\n\nTracking and disturbance rejection\n\nKalman filter and duality\n\nThe LQG design\n\nNonholonomic System Optimal Control\"\n\nF)Robotics \u0026  Robot Operating System-\n\n1.Mobole Robotics \u0026 sensing:\na.https://www.youtube.com/playlist?list=PLgnQpQtFTOGSeTU35ojkOdsscnenP2Cqx\n\nb.Mobile Sensing and Robotics 1 \u0026 2(Summer 2019 \u0026 2020, Uni Bonn)-\n\nhttps://www.youtube.com/playlist?list=PLgnQpQtFTOGQJXx-x0t23RmRbjp_yMb4v\n\nhttps://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6\n\n2.ROS:\nhttps://www.youtube.com/playlist?list=PLK0b4e05LnzZWg_7QrIQWyvSPX2WN2ncc\n\nhttps://www.youtube.com/playlist?list=PLRG6WP3c31_U7TFGduEIJWVtkOw6AJjFf\n\nhttps://www.youtube.com/playlist?list=PLE-BQwvVGf8HOvwXPgtDfWoxd4Cc6ghiP\n\nROS (Robotic OS) \n👉 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.\n\nArtificial Intelligence\n👉 I highly recommend Artificial Intelligence for Robotics by Udacity for a practical free introduction to self-driving cars.\n\n\nG) Advanced C++ for computer vision,linux Cmake(build), git(Version control), gtest, gitlab(Project Management):\nUniversity of Bonn:\n\n1.\nhttps://www.youtube.com/playlist?list=PLgnQpQtFTOGRM59sr3nSL8BmeMZR9GCIA\n\n2.\nhttps://www.youtube.com/playlist?list=PLgnQpQtFTOGR50iIOtO36nK6aNPtVq98C\n\nC++ - The Main Language\n👉 C++ Nanodegree, Udacity\nRead my C++ interview with Udacity to launch this course here.\n\n👉 Beginning C++ Programming - From beginner to beyond, Udemy Course\n\nPython - Useful to have\nYou shouldn't spend too much time on it, especially if you already have programming skills.\n👉 Complete Python Bootcamp: Go from Zero to hero in Python 3\n👉 Python Programming Masterclass\n\nBasic Linux Command Lines\n👉 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.\n\nThese were languages, the other very important thing is maths.\nIntermediate Probability\nIntermediate Calculus\nIntermediate Linear Algebra\n👉 You can learn these on Khan Academy\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMayakshanesht%2FAutonomous_Driving_Lecture_resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMayakshanesht%2FAutonomous_Driving_Lecture_resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMayakshanesht%2FAutonomous_Driving_Lecture_resources/lists"}