Self Driving Car Engineer

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Hear from Waymo, one of the most cutting-edge autonomous vehicle companies out there! You'll learn about the company as well as about the Waymo Open Dataset, which you'll use in parts of the program.

Dive into Deep Learning for Computer Vision, learning about its use cases, history, and what you’ll build by the end of the course.

Machine learning is more than just building a model - getting each step of the workflow right is crucial.

Learn how to calibrate your camera to remove distortions for improved perception.

Build skills in linear and logistic regression before taking on feedforward neural networks, a type of deep learning.

Convolutional networks improve on feedforward networks for areas such as image classification - let’s get started building them!

Object detection builds on classification by finding multiple important objects within a single image - find out how!

Use the Waymo dataset to detect objects in an urban environment.

Get started with sensor fusion and perception, why they are important, and the history of their development in self-driving cars.

Learn about the lidar sensor, capable of capturing important 3D data in point clouds.

Detect objects from the 3D data coming in from a lidar sensor.

Use the Waymo dataset to detect 3D objects in the surrounding environment.

Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.

Build an Extended Kalman Filter that's capable of handling data from multiple sources.

Get your tracking skills ready for the real world by learning how to track multiple targets simultaneously.

Use the Waymo dataset, along with sensor fusion, to track multiple 3D objects in the surrounding environment.

Meet the team that will guide you through the localization lessons, and learn the intuition behind robotic localization!

Are you ready to build Kalman Filters with C++? Take these quizzes to find out!

Learn the math behind localization, as well as how to implement Markov localization in C++.

Learn about and build two scan matching algorithms for localization: Iterative Closest Point (ICP) and Normal Distributions Transforms (NDT).

Learn how to align point clouds with ICP and NDT before leveraging them to localize a self-driving car in a simulated environment!

Localize a self-driving car within a point cloud from the CARLA simulator with the localization algorithms you learned in previous lessons - how accurate is your algorithm?

Learn how to think about high-level behavior planning in a self-driving car.

Use C++ and the Eigen linear algebra library to build candidate trajectories for the vehicle to follow.

Learn about and how to use PID controllers with Sebastian!

While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!

Use data from sensor fusion to generate predictions about the likely behavior of moving objects.

In this lesson, you'll learn about kinematic and dynamic vehicle models. We'll use these later with Model Predictive Control.

In this lesson, you'll learn how to frame the control problem as an optimization problem over time horizons. This is Model Predictive Control!

Learn about the Point Cloud Library (PCL). Use a simulation highway environment to explore lidar sensing and generate point clouds.

In this lesson you'll learn the motivation for Fully Convolutional Networks and how they are structured.

In this lesson you'll be introduced to the problem of Scene Understanding and the role FCNs play.

In this lesson you'll become familiar with various optimizations in an effort to squeeze every last bit of performance at inference.

You will learn to make safer vehicles using risk evaluation and systems engineering.

A functional safety plan is critical to any functional safety project. Here you will learn what goes into a safety plan so that you can document your own.

In a hazard analysis and risk assessment, you will identify vehicular malfunctions and evaluate their risk levels. You can then derive safety goals defining how your vehicle will remain safe.

You will derive functional safety requirements from the safety goals and then add extra functionality to the system diagram. Finally you document your work, a part of functional safety.

Once you have derived functional safety requirements, you drill down into more detail. In the technical safety concept, you refine your requirements into technical safety requirements.

The last step in the vehicle safety design phase is to derive hardware and software safety requirements. In this lesson, you will derive these requirements and refine a software system architecture.

Start off with some tips on interviewing for an autonomous systems role, then watch how candidates approach their interview questions. Finish off by practicing some questions of your own!