Self-Driving Car Engineer (Training)

Colleagues, become a Self-Driving Car Engineer and accelerate your career growth. Self-driving cars are transformational technology, on the cutting-edge of robotics, machine learning and engineering. Learn the skills and techniques used by self-driving car teams at the most advanced technology companies in the world. Learn the techniques that power self-driving cars across the full stack of a vehicle’s autonomous capabilities. Using Deep Learning with radar and lidar sensor fusion, you will train the vehicle to detect and identify its surroundings to inform navigation.. Prerequisite knowledge of  Python, C++, Linear Algebra and Calculus is desired.  Training modules with a hands-on project include: 1) Computer Vision - develop critical Machine Learning skills that are commonly leveraged in autonomous vehicle engineering. You will learn about the life cycle of a Machine Learning project, from framing the problem and choosing metrics to training and improving models.You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. (Project: Object Detection in an Urban Environment); 2) Sensor Fusion - besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability. Also learn how to fuse camera and lidar detections and track objects over time with an Extended Kalman Filter. Gain a solid foundation to work as a sensor fusion engineer on self-driving cars (Project: 3D Object Detection); 3) Sensor Fusion-Localization - learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. Iterative Closest Point (ICP) and Normal Distributions Transform (NDP), which work with 2D and 3D data - utilize these scan matching algorithms in the Point Cloud Library (PCL) to localize a simulated car with lidar sensing, using a 3D point cloud map obtained from the CARLA simulator (Project: Scan Matching Localization); 4) Planning - route vehicles from one point to another, and it handles how to react when emergencies arise. The Mercedes-Benz Vehicle Intelligence team will take you through the three stages of path planning. Generate a safe and comfortable trajectory to execute that maneuver (Project: Motion Planning and Decision Making for Autonomous Vehicle); and 5) Control - control a car once you have a desired trajectory. In other words, how to activate the throttle and the steering wheel of the car to move it following a trajectory described by coordinates -  most common controller: the Proportional Integral Derivative or PID controller. You will understand the basic principle of feedback control and how they are used in autonomous driving techniques.(Project: Control and Trajectory Tracking for Autonomous Vehicles)

Sign-up today (teams & execs welcome): https://tinyurl.com/pabx6dkw 

Much career success, Lawrence E. Wilson - Online Learning Central