# Self-Driving Car Engineer - Path Planning

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Predicting the environment and planning motion through it

This course is a part of the Self-Driving Car Engineer Nanodegree Program.

Path planning is the brains of a self-driving car. It’s how a vehicle decides how to get where it’s going, both at the macro and micro levels. You’ll learn about three core components of path planning: environmental prediction, behavioral planning, and trajectory generation. Best of all, this module is taught by our partners at Mercedes-Benz Research & Development North America. Their participation ensures that the module focuses specifically on material job candidates in this field need to know.

###### Included in Course
• Rich Learning Content

• Interactive Quizzes

• Taught by Industry Pros

• Real World Projects

• Student Support Community

• Personalized Career Support

## Join the Path to Greatness

This course is part of a Nanodegree Program. It is a step towards a new career in Self-Driving Car Engineer.

#### Self-Driving Car Engineer - Path Planning

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Enhance your skill set and boost your hirability through innovative, independent learning.

## What You Will Learn

#### Search

• In this lesson you will learn about discrete path planning and algorithms for solving the path planning problem.

#### Search

• In this lesson you will learn about discrete path planning and algorithms for solving the path planning problem.

#### Prediction

• In this lesson you'll learn how to use data from sensor fusion to generate predictions about the likely behavior of moving objects.

#### Prediction

• In this lesson you'll learn how to use data from sensor fusion to generate predictions about the likely behavior of moving objects.

#### Behavior Planning

• In this lesson you'll learn how to think about high level behavior planning in a self driving car.

#### Behavior Planning

• In this lesson you'll learn how to think about high level behavior planning in a self driving car.

#### Trajectory Generation

• In this lesson, you’ll use C++ and the Eigen linear algebra library to build candidate trajectories for the vehicle to follow.

#### Trajectory Generation

• In this lesson, you’ll use C++ and the Eigen linear algebra library to build candidate trajectories for the vehicle to follow.

#### Path Planning Project

• Drive a car down a highway with other cars using your own path planner.

#### Path Planning Project

• Drive a car down a highway with other cars using your own path planner.

## Prerequisites and Requirements

C++, Calculus

See the Technology Requirements for using Udacity.

## Why Take This Course

By the end of this course, you will be able to predict object motion using a Gaussian Naive Bayesian Classifer, plan your own vehicle's behavior using a finite state machine, and generate trajectories using quintic polynomials.

###### What do I get?
• Instructor videos
• Learn by doing exercises
• Taught by industry professionals

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