More Udacity Self-Driving Car Students, In Their Own Words

Yesterday I shared 5 amazing blog posts by students in the Udacity Self-Driving Car Nanodegree Program.

Here are 5 more!

DeepTrafficJS Solution

Anton Pechenko

Anton is a student in the Udacity Self-Driving Car Program, and also in MIT’s class on Deep Learning for Self-Driving Cars. He is currently third in MIT’s Deep Traffic competition, and he reviews his deep neural network in this video. Pay attention to his choice of activation functions!

My first self-driving car

Bill Zito

Bill has a really nice walkthrough of some of the key lessons he learned while completing the final project in the Deep Learning Module. This project, Behavioral Cloning, requires students to drive a car in a simulator, record their driving data, use that data to train a neural network, and then use that network to drive the car.

This challenge is no walk in the park, and that’s part of what makes it really fun. You’re implementing similar code to the code that drives self-driving cars in real life. And you’re required to think through lots of the steps of the process by yourself to do so. If you end up stuck, remember that the experts just figured out this stuff was even possible in the last couple years.

German Traffic Sign Classification Using Deep Learning

Muddassir Ahmed

Muddassir gives a great explanation of what a neural network is, and what a convolutional neural network is — including the history behind them! Then he explains how he implemented the Traffic Sign Classifier Project.

The CNN was inspired by the work of Hubel and Wiesel back in the 1950s and 1960s. In the study, they discovered that the mammalian brain was structured hierarchically and that objects were recognized based on the hierarchical build-up of features from small ones, such as colors, stripes, lines into bigger ones such as patterns and even larger concepts of dog, cat, human e.t.c.

Self-Driving Car Engineer Diary — 2

Andrew Wilkie

We put a lot of effort into making the first week of the program fun and rewarding, so students understand from the very beginning what they might be able to accomplish. Andrew has a great journal of how his first week in the program went.

Andrew Gray popped into our student ‘ama’ Slack channel for a 30 minute Q&A. Students were asking a lot about future job opportunities in this new Self-Driving vehicle industry. Some were concerned with the rapid rate of improvement and that by the time we graduate (Sep/2017 for my Dec/2016 co-hort) that we might have missed the best opportunities. Andrew highlighted the fact many existing companies outside of Self-Driving cars and trucks are actively pursuing combined AI / Robotics strategies and completing this intensive 9 month training readies us for this new industry.

Udacity Self-Driving Car Nanodegree Project 1 — Finding Lane Lines

Jeremy Shannon

Jeremy says some really nice things about the Udacity program in his post, and then outlines the steps he took to complete the Finding Lane Lines Project. This was my own very first project as an autonomous vehicle engineering student, so it is near and dear to my heart.

This is the best online course (or, should I say, collection of courses) I’ve taken so far. Yes, even better than Fire Safety Refresher Training. Really! The quality is top-notch (both video and written/supplemental material), the feedback is amazing, and the community they’ve built around it is incredibly helpful. (I wish that during my undergrad days I’d had an online forum I could go to and find that dozens of other students were having the same problem I was having.) It’s so easy to become completely immersed in the subject material, and I’m so thankful that this program exists. Udacity has really outdone themselves and I can’t possibly heap enough praise on them.

Self-Driving Car Student Posts

One of the most fun things about the Udacity Self-Driving Car Engineer Nanodegree Program is the student community. Thousands of students are active on Slack and in the forums, helping each other complete projects, understand concepts, and get jobs in the autonomous vehicle industry.

Students also write online about their experiences in the program and what they’ve accomplished. Here are five, in their own words.


Behavioral Cloning for Autonomous Vehicles

David Ventimiglia

It often pays to explore your data with relatively few constraints before diving in to build and train the actual model. One may gain insights that help guide you to better models and strategies, and avoid pitfalls and dead-ends.


A transforming reality — Udacity’s Self Driving Car Nano-degree

Vishal Rangras

I was so much into this program that I started talking about it with senior folks at my workplace. They encouraged me, supported me and appreciated my love for the technology and research. They got interested in knowing more about the program. They got stunned about this state-of-the-art program and they wanted to help me pursue my dream of research. They extended a helping hand to me by arranging an online fundraiser to support me.


Studying for the Udacity SDCND, or How I got my law license

Michael Toback

Udacity has a great program, but they refer you to other sources as you go, both inside and outside of Udacity. Hurrying through it won’t help you, because people need time to develop and strengthen the neural pathways that help you really learn something.


Why I enrolled in Udacity Self-Driving Car Classes

Boris Dayma

I’m currently in a very different industry than tech and automotive (I’m actually in the oil & gas industry). However I’ve always tried to apply latest innovations to assist me in my daily activities. This has helped me in automating the “boring tasks” to let me focus on more fun challenges, leading to new creative solutions.


Experiment Using Deep Learning to find Road Lane Lines

Paul Heraty

I modified a neural network that I had used in the SDCND BehavioralCloning lab (5 CNN layers followed by 3 FCNN layers), and added 5 new outputs to it. So now the network looks like 5 CNN layers with 6x 3 FCNN layers. The outputs are generating lane polynomial coefficients for both the left and right lanes, i.e. a*y² + b*y + c where I’m predicting a, b & c for each lane.

“Full Self-Driving” in 6 Months?

One of the most common questions I get from friends and relatives is, “How soon will we get self-driving cars?”

And my answer is that it’s a question of where, not when. Uber already has tightly-geofenced autonomous vehicle trials in Pittsburgh, and nuTonomy and Delphi both have limited trials in Singapore. nuTonomy has announced more trials in Boston. Uber came to San Francisco and then left within a week, due to a dispute with the California DMV.

Over the next year, we’ll see these types of public trials roll out to even more cities.

Elon Musk, though, said something last night that might blow all of that out of the water.

That’s all I’ve got, and maybe Musk is over-promising. But it sounds pretty exciting.

News Roundup

California Is Sunsetting Botts’ Dots: Standardizing lane markings helps self-driving cars. If you don’t know what Botts’ Dots are, click through to learn a little bit about transportation history!

Nissan and Volvo to Test Self-Driving Cars in London This Year: It’s unclear whether these particular programs will be open to the public, but we will see more of these single-city tests this year.

Self-Driving Cars Will Bring Jobs to Disabled People: Disabled people have been staunch advocates of self-driving cars, for obvious reasons, but I never really thought through the economics of this. One estimate is that 2 million disabled Americans will become employable simply by being able to get to work.

Is Amazon Developing a Self-Driving Car?

Sean Everett makes a strident case that Amazon is developing it’s own self-driving vehicles.

I’ve heard rumors about this for a while, including from startups that wouldn’t mind one day being acquired by Amazon.

And it makes all the sense in the world.

But I’m not sure I believe it.

The main reason it’s such an open secret that Apple is working on self-driving cars is that self-driving car experts keep joining Apple to work on some secret project.

I haven’t heard anything like that about Amazon.

So if prominent autonomous vehicle engineers aren’t building Amazon’s self-driving vehicle program, then who is?

Autonomous Vehicle Customer Power

Porter’s Five Forces is one of the classic frameworks that business schools teach for evaluating the attractiveness of a business or industry.. Michael Porter, a professor at Harvard Business School, invented it.

The framework is, at its core, a five-item checklist:

  1. Barriers to Entry
  2. Substitution
  3. Supplier Power
  4. Customer Power
  5. Existing Competition

I’m re-wording Porter’s original definitions to the phrases I think of when I step through the checklist, but that’s the gist of it.

I thought of this checklist during a recent conversation about strategy for Waymo (Google’s Self-Driving Car division).

One school of thought is that Google should position itself as the supplier of software to the automotive industry. That’s a position that’s worked well for Android, and it plays to Google’s strengths as a software company.

An alternative school of thought is that Google should build out its own transportation-as-a-service business, because car manufacturers are too smart to get trapped with Google’s software. They’ve seen how that played out for the mobile handset manufacturers, and they don’t want to get “Samsungized”.

Rather than choose between these two approaches, I want to focus here on the narrow question of whether the car manufacturers are “too smart” to get trapped with Google’s software.

The people I worked with at Ford are very smart, and I imagine the same is true at Toyota and GM and most other manufacturers.

But this is where Porter’s five forces comes in.

As a automotive software vendor, Google is in an industry with low “customer power”. There are a lot of different car manufacturers.

Some of those manufacturers are going to be way behind the curve in terms of developing autonomous vehicle software. And those manufacturers will, rationally, decide that their best bet for catching up with the pack is to partner with Waymo.

So regardless of how smart the car manufacturers are, there will be manufacturers out there who will be interested in using Waymo’s software.

Deep Learning Nanodegree Foundation Program

Last night Udacity announced a Deep Learning Nanodegree Foundation Program, in partnership with Siraj Rival, who has been teaching deep learning on YouTube to a huge audience.

We’re really excited about this program, which is a little bit of an experiment for Udacity.

Deep learning is becoming an important component in autonomous vehicles, and in fact it forms the core of the first term of Udacity’s Self-Driving Car Engineer Nanodegree Program.

If you’re interested in joining the Udacity Self-Driving Car program, you might want to consider the new Deep Learning Nanodegree Foundation Program as a warm-up.

Any student who completes the Deep Learning program is guaranteed admission into the Self-Driving Car program, along with a $100 credit.

Waymo Debut

This year was the first CES appearance for Waymo, although it has appeared in previous years under the name, “Google Self-Driving Car Program”.

Waymo CEO John Krafcik used the opportunity to shed some light on a perpetual parlor game for self-driving car enthusiasts: “what is Google’s strategy for self-driving cars?”

Google has had self-driving cars scooting around Mountain View for years, but it’s always been unclear what the go-to-market strategy would be, and when it would start rolling.

Krafcik’s statements indicate that Google’s long game may be as a provider of autonomous vehicle hardware and software, rather than as an auto manufacturer or a transportation-as-a-service provider.

Krafcik specifically highlighted Waymo’s gains with its in-house LIDAR sensor technology. Along with adding short- and long-range sensing units for better detection than its previous systems, the company has managed to slash the cost of its LIDAR by more than 90 percent in two years from about $75,000 per vehicle.

…

Krafcik’s language is telling — those “firsts” imply that this is just the start of Waymo’s autonomous partnerships, with many more to come. By focusing on the production of its own hardware along with software, Waymo won’t become a major automaker, at least not yet. Instead, it clearly intends to be the go-to supplier of autonomous driving systems for automakers that don’t want the expense of developing their own.

I’m not convinced the mystery has been solved yet, but it’s an important clue in the Google guessing game.

Teleoperation

Nissan is considering building out its self-driving cars in conjunction with call centers staffed by representatives who can help the self-driving cars get out of sticky situations.

According to Wired, Nissan calls this system “teleoperation” and views it as unavoidable, at least in the short term. Weird things happen on the road and the car won’t be able to figure it all out on its own. If the car also doesn’t have a steering wheel, that leaves tele-drivers as the next best option.

On the one hand, this makes perfect sense. Wired draws an analogy to an elevator, which almost always has an emergency call button that presumably (I’ve never tried) dials somebody who can help.

The rub seems to come down to whether these call centers are staffed by representatives who only step in at critical junctures, or whether the vehicles are really “teleoperated”.

The latter doesn’t seem safe (latency being a big problem) and doesn’t seem like a big improvement over normal human driving, but it’s an interesting minimum viable product.

Frank Chen’s 16 Questions About Self-Driving Car

Frank Chen is a Partner at Andreesen Horowitz who publishes terrific summaries of exciting area of technology.

A while back we talked on the phone about a self-driving car presentation he was thinking of putting together.

He put it together and it’s a terrific discussion of 16 Questions About Self-Driving Cars.

  1. Straight the Level 5 or not?
  2. LIDAR or not?
  3. Pre-computed HD maps, or build on the fly?
  4. What blend of computation techniques?
  5. How much real world vs. virtual world testing?
  6. Will V2X radios play an important role?
  7. Can we get rid of traffic lights and four way stops?
  8. How will automakers “localize” their cars?
  9. How will accident rates trend?
  10. When will it become illegal to drive?
  11. How will insurance change?
  12. Who will win? Silicon Valley vs. China vs. Incumbents
  13. Will we buy cars or transportation as a service?
  14. How will commute times change?
  15. How will cities change?
  16. When will this start, and then how quickly will we change to autonomous cars?

Watch the whole presentation!