Self Driving Car Engineer

An Introduction to Your Nanodegree Program

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Meet Waymo

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.

Introduction to Deep Learning for Computer Vision

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

The Machine Learning Workflow

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

Sensor and Camera Calibration

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

From Linear Regression to Feedforward Neural Networks

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

Image Classification with CNNs

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

Object Detection in Images

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

Object Detection in an Urban Environment

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

Introduction to Sensor Fusion and Perception

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

The Lidar Sensor

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

Detecting Objects in Lidar

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

Mid-Term Project: 3D Object Detection

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

Kalman Filters

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

Extended Kalman Filters

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

Multi-Target Tracking

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

Final Project: Sensor Fusion and Object Tracking

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

Congratulations!

Introduction to Localization

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

C++ Checkpoint

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

Markov Localization

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

[Optional] Intro to PCL

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

Creating Scan Matching Algorithms

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

Utilizing Scan Matching

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

Scan Matching Localization

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?

Behavior Planning

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

Trajectory Generation

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

Motion Planning

Motion Planning and Decision Making for Autonomous Vehicles

PID Control

Learn about and how to use PID controllers with Sebastian!

Control and Trajectory Tracking for Autonomous Vehicles

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.

Optimize Your GitHub Profile

Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.

Unscented Kalman Filters

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!

Prediction

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

Vehicle Models

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

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!

Fully Convolutional Networks

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

Scene Understanding

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

Inference Performance

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

Introduction to Functional Safety

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

Functional Safety: Safety Plan

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.

Functional Safety: Hazard Analysis and Risk Assessment

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.

Functional Safety: Functional Safety Concept

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.

Functional Safety: Technical Safety Concept

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.

Functional Safety at the Software and Hardware Levels

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.

Autonomous Systems Interview Practice

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!