Download and Learn Self-Driving Car Engineer Udacity Nanodegree Course 2023 for free with google drive download link.
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.
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What You’ll Learn in Self-Driving Car Engineer Nanodegree
Self-Driving Car Engineer
5 months to complete
In this program, you will 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.
Self-Driving Car Engineer Nanodegree Intro:
Python, C++, Linear Algebra and Calculus.
A well prepared student will be able to:
- Build object-oriented programs in any language (ideally Python or C++)
- Compute integrals and derivatives of polynomial functions
- Multiply matrices and understand related aspects of linear algebra
- Calculate mean, median, and standard deviation of a dataset
- Model the effects of forces on point masses
For aspiring self-driving car engineers who currently have a limited background in programming, math, computer vision, or machine learning, we’ve created the Introduction to Self-Driving Cars Nanodegree Program to help them prepare.
In this course, you will 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. This course will focus on the camera sensor and you will learn how to process raw digital images before feeding them into different algorithms, such as neural networks. You will build convolutional neural networks using TensorFlow and learn how to classify and detect objects in images. With this course, you will be exposed to the whole Machine Learning workflow and get a good understanding of the work of a Machine Learning Engineer and how it translates to the autonomous vehicle context.
Project – Object Detection in an Urban Environment
In this project, students will create a convolutional neural network to detect and classify objects using data from the Waymo Open Dataset. Students will be provided with a dataset of images of urban environments containing annotated cyclists, pedestrians and vehicles. First, they will perform an extensive data analysis including the computation of label distributions, display of sample images, and checking for object occlusions. Students will use this analysis to decide what augmentations are meaningful for this project. Then, they will train a neural network to detect and classify objects. Students will monitor the training with TensorBoard and decide when to end it. Finally, they will experiment with different hyperparameters to improve performance.
In this course, you will learn about a key enabler for self-driving cars: sensor fusion. Besides cameras, self-driving cars rely on other sensors with complementary measurement principles to improve robustness and reliability. Therefore, you will learn about the lidar sensor and its role in the autonomous vehicle sensor suite. You will learn about the lidar working principle, get an overview of currently available lidar types and their differences, and look at relevant criteria for sensor selection. Also, you will learn how to detect objects such as vehicles in a 3D lidar point cloud using a deep-learning approach and then evaluate detection performance using a set of state-of-the-art metrics. In the second half of the course, you will learn how to fuse camera and lidar detections and track objects over time with an Extended Kalman Filter. You will get hands-on experience with multi-target tracking, where you will learn how to initialize, update and delete tracks, assign measurements to tracks with data association techniques and manage several tracks simultaneously. After completing the course, you will have a solid foundation to work as a sensor fusion engineer on self-driving cars.
Project – 3D Object Detection
Students will first load and preprocess 3D lidar point clouds, and then use a deep learning approach to detect and classify objects (e.g. vehicles, pedestrians). Students will evaluate and visualize the objects, including calculating key performance metrics. This project combines with the Sensor Fusion project to form a full detection pipeline.
Project – Sensor Fusion
In this project, students will solve a challenging multi-target tracking task by fusing camera and lidar detections. They will implement an Extended Kalman filter to track several vehicles over time, including the different measurement models for camera and lidar. This also requires a track management module for track initialization and deletion, and a data association module to decide which measurement originated from which
track. Finally, students will evaluate and visualize the tracked objects. To complete this project, students will use a real-world dataset and therefore face many everyday challenges of a sensor fusion engineer.
In this course, you will learn all about robotic localization, from one-dimensional motion models up to using three-dimensional point cloud maps obtained from lidar sensors. You’ll begin by learning about the bicycle motion model, an approach to use simple motion to estimate location at the next time step, before gathering sensor data. Then, you’ll move onto using Markov localization in order to do 1D object tracking, as well as further leveraging motion models. From there, you will learn how to implement two scan matching algorithms, Iterative Closest Point (ICP) and Normal Distributions Transform (NDP), which work with 2D and 3D data. Finally, you will 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
In this project, students will use either ICP or NDT, two scan matching algorithms, to align point cloud scans from the CARLA simulator and recover the position of a simulated car with lidar. Students will need to achieve sufficient accuracy for the entirety of a drive within the simulated environment, updating the vehicle’s location appropriately as it moves and obtains new lidar data.
Path planning routes a vehicle 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. First, you’ll apply model-driven and data-driven approaches to predict how other vehicles on the road will behave. Then you’ll construct a finite state machine to decide which of several maneuvers your own vehicle should undertake. Finally, you’ll generate a safe and comfortable trajectory to execute that maneuver.
Project – Motion Planning and Decision Making for Autonomous Vehicles
In this project, you will implement two of the main components of a traditional hierarchical planner: the behavior planner and the motion planner. Both will work in unison to be able to avoid static objects parked on the side of the road, avoid crashing with these vehicles by executing either a “nudge” or a “lane change” maneuver, handle any type of intersection, and track the centerline on the traveling lane.
This course will teach you how to 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. The course will cover the most basic but also the 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
In this project, you will apply the skills you have acquired in this course to design a PID controller to perform vehicle trajectory tracking. Given a trajectory as an array of locations, and a simulation environment, you will design and code a PID controller and test its efficiency on the CARLA simulator used in the industry. This project will help you to understand the power and the limitations of the PID controller and how feedback control is used in practice. Finally, this project is a good training for C++ coding which is the language used in the industry.
As a self-driving car engineer, you have the potential to help save over 1 million people per year!
Self-Driving Car Engineer Nanodegree Free Download Link: