Month 1: Path Planning

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.

Path Planning Lesson 1: Environmental Prediction

In the Prediction Lesson, you’ll use model-based, data-driven, and hybrid approaches to predict what other vehicles around you will do next. Model-based approaches decide which of several distinct maneuvers a vehicle might be undertaking. Data-driven approaches use training data to map a vehicle’s behavior to what we’ve seen other vehicles do in the past. Hybrid approaches combine models and data to predict where other vehicles will go next. All of this is crucial for making our own decisions about how to move.

Path Planning Lesson 2: Behavior Planning

At each step in time, the path planner must choose a maneuver to perform. In the Behavior Lesson, you’ll build finite-state machines to represent all of the different possible maneuvers your vehicle could choose. Your FSMs might include accelerate, decelerate, shift left, shift right, and continue straight. You’ll then construct a cost function that assigns a cost to each maneuver, and chooses the lowest-cost option.

Path Planning Lesson 3: Trajectory Generation

Trajectory Generation is taught by Emmanuel Boidot, from Mercedes-Benz’s Vehicle Intelligence team.

In the Trajectory Lesson, you’ll use C++ and the Eigen linear algebra library to build candidate trajectories for the vehicle to follow. Some of these trajectories might be unsafe, others might simply be uncomfortable. Your cost function will guide you to the best available trajectory for the vehicle to execute.

Path Planning Project: Highway Path Planner

Using the newest release of the Udacity simulator, you’ll build your very own path planner and put it to the test on the highway. Tie together your prediction, behavior, and trajectory engines from the previous lessons to create an end-to-end path planner that drives the car in traffic!

Month 2: Electives

Term 3 will launch with two electives: Advanced Deep Learning, and Functional Safety. We’ve selected these based on feedback from our hiring partners, and we’re very excited to give students the opportunity to gain deep knowledge in these topics.

Month 2 Elective: Advanced Deep Learning

Udacity has partnered with the NVIDIA Deep Learning Institute to build an advanced course on deep learning.

This module covers semantic segmentation, and inference optimization. Both of these topics are active areas of deep learning research.

Semantic segmentation identifies free space on the road at pixel-level granularity, which improves decision-making ability. Inference optimizations accelerate the speed at which neural networks can run, which is crucial for computational-intense models like the semantic segmentation networks you’ll study in this module.

Advanced Deep Learning Lesson 1: Fully Convolutional Networks

In this lesson, you’ll build and train fully convolutional networks that output an entire image, instead of just a classification. You’ll implement three special techniques that FCNs use: 1x1 convolutions, upsampling, and skip layers, to train your own FCN models.

Advanced Deep Learning Lesson 2: Scene Understanding

In this lesson, you’ll learn the strengths and weaknesses of bounding box networks, like YOLO and Single Shot Detectors. Then you’ll go a step beyond bounding box networks and build your own semantic segmentation networks. You’ll start with canonical models like VGG and ResNet. After removing their final, fully-connected layers, you can add the three special techniques you’ve already practiced: 1x1 convolutions, upsampling, and skip layers. Your result will be an FCN that classifies each road pixel in the image!

Advanced Deep Learning Lesson 3: Inference Optimizations

One of the challenges of semantic segmentation is that it requires a lot of computational power. In this lesson, you’ll learn how to accelerate network performance in production, using techniques such as fusion, quantization, and reduced precision.

Advanced Deep Learning Project: Semantic Segmentation

In the project at the end of the Advanced Deep Learning Module, you’ll build a semantic segmentation network to identify free space on the road. You’ll apply your knowledge of fully convolutional networks and their special techniques to create a semantic segmentation model that classifies each pixel of free space on the road. You’ll accelerate the network’s performance using inference optimizations like fusion, quantization, and reduced precision. You’ll be studying and implementing approaches used by top performers in the KITTI Road Detection Competition!

Month 2 Elective: Functional Safety

Together with Elektrobit, we’ve built a fun and comprehensive Functional Safety Module.

You’ll learn functional safety frameworks to ensure that vehicles is safe, both at the system and component levels.

Functional Safety Lesson 1: Introduction

You’ll build a functional safety case with Dheeraj, Stephanie, and Benjamin from Elektrobit.

In this lesson, Elektrobit’s experts will guide you through the high-level steps that the ISO 26262 standard requires for building a functional safety case. ISO 26262 is the world-recognized standard for automotive functional safety. Understanding the requirements of this standard gets you started on mastering a crucial field of autonomous vehicle development.

Functional Safety Lesson 2: Safety Plan

In this lesson, you’ll build a safety plan for a lane-keeping assistance feature. You’ll start with the same template that Elektrobit functional safety managers use, and add the information specific to your feature.

Functional Safety Lesson 3: Hazard Analysis and Risk Assessment

You’ll complete a hazard analysis and risk assessment for the lane-keeping assistance feature. As part of the HARA, you’ll brainstorm how the system might fail, including the operational mode, environmental details, and item usage of each hypothetical scenario. Your HARA will record the issues to monitor in your functional safety analysis.

Functional Safety Lesson 4: Functional Safety Concept

For each issue identified in the HARA, you’ll develop a functional safety concept that describes high-level performance requirements.

Functional Safety Lesson 5: Technical Safety Concept

You’ll translate high-level functional safety concept requirements into technical safety concept requirements that dictate specific performance parameters. At this point you’ll have concrete constraints for the system.

Functional Safety Lesson 6: Software and Hardware

Functional safety includes specific rules on how to implement hardware and software. In this lesson, you’ll learn about spatial, temporal, and communication interference, and how to guard against them. You’ll also review MISRA C++, the most common set of rules for writing C++ for automotive systems.

Functional Safety Project: Safety Case

You’ll use the guidance from your lessons to construct an end-to-end safety case for a lane departure warning feature. You’ll begin with the hazard analysis and risk assessment, and create further documentation for functional and technical safety concepts, and finally software and hardware requirements. Analyzing and documenting system safety is critical for autonomous vehicle development. These are skills that often only experienced automotive engineers possess!

System Integration

System integration is the final module of the Nanodegree program, and it’s the month where you actually get to put your code on the Udacity Self-Driving Car!

You’ll learn about the software stack that runs on “Carla,” our self-driving vehicle. Over the course of the final month of the program, you will work in teams to integrate software components, and get the car to drive itself around the Udacity test track.

Vehicle Subsystems

This lesson walks you through Carla’s key subsystems: sensors, perception, planning, and control. Eventually you’ll need to integrate software modules with these systems so that Carla can navigate the test track.

ROS and Autoware

Carla runs on two popular open-source automotive libraries: ROS and Autoware. In this lesson you’ll practice implementing ROS nodes and Autoware modules.

System Integration

During the final lesson of the program, you’ll integrate ROS nodes and Autoware modules with Carla’s software development environment. You’ll also learn how to transfer the code to the vehicle, and resolve issues that arise on real hardware, such as latency, dropped messages, and process crashing.

Project: Carla

This is the capstone project of the Nanodegree program! You will work with a team of students to integrate the skills you’ve developed over the last nine months. The goal is to build Carla’s software environment to successfully navigate Udacity’s test track.