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Autonomous Systems

We’ve designed our autonomous systems courses in self-driving car engineering and sensors in collaboration with industry-leading partners like Mercedes-Benz, Nvidia, and McLaren to ensure a first-in-class learning experience. Companies in the automotive, manufacturing, and warehousing industries have used Udacity to train their employees in the development and deployment of automated systems.

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In Demand Skill Sets

Learn the autonomous systems engineering skills you need to start or advance a career building self-driving cars and trucks, including Python, C++, ROS, Kalman filters, and more.

Steps To Become A Self-Driving Car Engineer

  • Introduction to Self-Driving Cars


    3 months

    , Intermediate

    Step 1

    Skills Covered

    Probability distribution, Python syntax, Object localization, Matrix operations, C++ code optimization, Computer vision fluency, Image transformations, Derivatives, C++ syntax, Basic probability, Binary classification models, Feature detection, Pythagorean theorem, Python data structures, Bayes' theorem, Feature extraction, Image classification, C++ libraries, Kalman filters, Image pre-processing, Machine learning fluency, Robot navigation, Integrals, Accelerometers, Robot state management, Object-oriented programming basics, Python arrays, Gaussian distribution, Data augmentation, Data pre-processing for ML, Graph algorithms, Search algorithms, Computer memory architecture, Memory management in c++, C++ functions, Programming language implementations, C++ classes, Programming language type systems, C++ control flow, Python classes, Conditional probability, Sine, Odometers, Cosine, Algorithmic problem solving, Uniform cost search, Greedy algorithms, Breadth-first search, Search problems, Python collections, Depth-first search, A* search algorithm, Tree search, Python lists, C++ vectors, Vectors, Python methods, Object motion models, Matrix multiplication, State transformations, Python functions, matplotlib, NumPy, Bayesian probability, Histogram filters, Code debugging

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  • (729)

    3 months

    , Intermediate

    Step 2


    3 months

    , Intermediate

    Skills Covered

    Threading, Concurrent computing, Computer memory architecture, Dynamic memory allocation, C++ copy and move semantics, Heap memory, C++ polymorphism, A* search algorithm, Control flow, C++ syntax, C++ data structures, Futures and promises, Parallel computing, Data races, Message queues, Memory management in c++, R-values, Smart pointers, Buffer overruns, Memory-efficient programming, Call-by-value, Rule of three, Memory leaks, L-values, Code debugging, Segmentation fault, Automatic memory allocation, Hexadecimal numbers, Variable scope, Call-by-reference, Rule of five, Memory fragmentation, Virtual memory, Stack memory, Ownership in memory management, Uninitialized memory bugs, Call stacks, Raii, Encapsulation, Access specifiers, Inheritance, Abstraction, Generic programming, C++ data types, C++ file handling, C++ constants, Software project organization, C++ user input, C++ header files, Cmake, C++ pointers

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  • Step 3

    Skills Covered

    Robot behavior planning, Object motion models, Robot motion planning, Eigen library, A* search algorithm, Robot trajectory generation, C++ syntax, Logistic regression, Model performance metrics, Camera calibration, Image transformations, Bayes' filter for localization, 1d object localization, Gradient descent, Bayes' theorem, Point clouds, Computer vision fluency, Feedforward neural networks, Waymo open dataset, Model evaluation, Automated path planning, Collision detection, 2d object localization, TensorFlow, Image classification, Backpropagation, Control algorithms, Object state estimation, Object perturbations, Iterative closest point, Law of total probability, Normal distributions transform, Lidar sensors, Point cloud library, Convolutional neural networks, AI business context, Tensorflow object detection API, Mpc controller, Vehicle action, Lidar, Linear regression, Tensors, Machine learning pipeline fundamentals, Cameras, Radar, Pid controller, Velocity profile generation, C++ control flow, Lidar visualization, Git, C++ classes, Lidar simulator, C++ functions, C++ standard library, Exploratory data analysis, Pillow, Object detection, Color models, Keras, Yolo algorithm, Deep learning, Overfitting prevention, Color spaces, Data augmentation, Computer vision image enhancement, Computer vision image filtering, Sensors, Bird's eye view maps, Sae j3016 levels of driving automation standard, Object tracking performance metrics, Waymo lidar specifications, Camera measurement models, Extended kalman filters, Lidar technical specifications, Object track score, Jacobians, Region-based convolutional neural networks, Computer vision benchmarks, Object tracking, Object track state, Root-mean-square error, Multi-target object tracking, Sensor visibility reasoning, Coordinate transformation, Range imaging, Lidar measurement models, Kalman filters, Feature extraction, Autonomous vehicle sensor selection

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Popular Autonomous Systems Programs

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Autonomous Systems Across Industries

  • Automotive & Trucking

    • Autonomous driving (AD) systems are a rapidly advancing technology, yet there are still significant technical challenges to getting AD vehicles on the road.
    • From developing hands-off but eyes-on-the-road AD that assists drivers to building fully self-driving trucks, automakers need talent with the skills to build these systems now more than ever.
  • Suppliers & OEMs

    • Automotive suppliers must keep pace with industry changes to deliver the hardware and software required by cars of the future, like electric drivetrains, driver assistance, and electric vehicle (EV) batteries.
    • Tier 1 suppliers—those who sell parts directly to automakers—know they must future-proof their workforces as they face competition from numerous Silicon Valley startups.
  • Manufacturing

    • Robotics and automation technology improve safety, speed, and consistency in the manufacturing industry.
    • These technologies also free up human workers from mundane, repetitive tasks and create additional opportunities for innovation.
  • Connected Vehicles

    • Connected vehicles—part of the ever-evolving field of the Internet of Things (IoT)—will soon be able to perform tasks like communicating with street lights and other infrastructure and aid in making daily purchases like gas and groceries.
    • Cars will also monitor maintenance issues such as tire pressure and oil quality, as well as suggest locations for service and pricing.

Programs Co-created With Autonomous Systems Leaders.


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