HN Academy

While Kalman and Bayesian Filters in Python is a superb resource, probably the best out there, my recommendation for anyone new to the field would be to do Sebastian Thrun's free Artificial Intelligence for Robotics course [1] as an intro, then go through Labbe's work afterwards.

Thrun's course is more accessible and even more hands-on than Labbe's content. As a bonus he also covers Particle Filters,PID control, Search and SLAM (which cam out of Thrun's PhD thesis).

[1] https://www.udacity.com/course/artificial-intelligence-for-r...

A while back I wanted to learn about SLAM, computer vision, robotics, drones, etc. I did all the following courses which had significant overlap in materials:

https://www.edx.org/course/autonomous-navigation-flying-robo...

https://www.coursera.org/specializations/robotics (also available at edx as https://www.edx.org/micromasters/pennx-robotics)

https://in.udacity.com/course/artificial-intelligence-for-ro...

And I would rank them in the order of edx, coursera, and udacity last.

It's not totally fair because the best comparison for udacity should be their self driving car nanodegree but it doesn't let you audit for free and I don't care for certificates. From the few udacity courses I did do, I felt their videos are too short and triggers my ADHD to go do something else after watching each minute or two long video. Edx/coursera felt a lot more like university lectures and felt more rigorous in comparison.

⬐ capitalsigma

I found edX to be pretty lacking if you wanted to learn something advanced that's not quite "flavor of the day:" no higher level pure math classes, very few algorithms classes past intro level, very little material on digital design other than the MIT 6.002 sequence. Tons of choices for machine learning, Python, and AP calculus though.

⬐ barry-cotter

There’s a reasonably extensive MicroMasters course on Algorithms and Data Structures. How would you rate it? Comparable to finishing Skiena? CLRS? Concrete Mathematics?

https://www.edx.org/micromasters/ucsandiegox-algorithms-and-...

What You'll Learn:

Understand essential algorithmic techniques and apply them to solve algorithmic problems Implement programs that work in less than one second even on massive datasets Test and debug your code even without knowing the input on which it fails Formulate real life computational problems as rigorous algorithmic problems Prove correctness of an algorithm and analyze its running time

Courses Algorithmic Design and Techniques Learn how to design algorithms, solve computational problems and implement solutions efficiently.

Data Structures Fundamentals Learn about data structures that are used in computational thinking – both basic and advanced.

Graph Algorithms Learn how to use algorithms to explore graphs, compute shortest distance, min spanning tree, and connected components.

NP-Complete Problems Learn about NP-complete problems, known as hard problems that can’t be solved efficiently, and practice solving them using algorithmic techniques.

String Processing and Pattern Matching Algorithms Learn about pattern matching and string processing algorithms and how they apply to interesting applications.

Dynamic Programming: Applications In Machine Learning and Genomics Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.

Graph Algorithms in Genome Sequencing Learn how graphs are used to assemble millions of pieces of DNA into a contiguous genome and use these genomes to construct a Tree of Life.

Algorithms and Data Structures Capstone Synthesize your knowledge of algorithms and biology to build your own software for solving a biological challenge.

⬐ ericlavigne

Agreed that Udacity's AI for Robotics was a weak course. I highly recommend some of Udacity's recent paid-only courses on robotics:

Self-driving car engineer nanodegree (not the one with "intro" in its name) https://www.udacity.com/course/self-driving-car-engineer-nan...

Robotics software engineer nanodegree https://www.udacity.com/course/robotics-software-engineer--n...

Flying car and autonomous flight engineer nanodegree https://www.udacity.com/course/flying-car-nanodegree--nd787

Just as a sample, this is one of many projects I completed as part of the self-driving car engineer nanodegree. My code controls a car driving on a highway with other cars.

https://github.com/ericlavigne/CarND-Path-Planning

> I'm currently working through Andrew Ng's ML course on Coursera. It's definitely high-level

I agree that it is "high level" and glosses over (purposefully) the nitty-gritty details of the "black boxes" for the most part. I say this as someone who took the first incarnation of the course, which was known as "ML Class" in the fall of 2011, before Coursera came about.

Despite it being high-level, though - this is what one of my "classmates" was able to create, about halfway or so thru the course:

http://blog.davidsingleton.org/nnrccar/

In 2012, I completed Udacity's CS373 course (https://www.udacity.com/course/artificial-intelligence-for-r...).

Today, I'm currently in the second term of Udacity's Self-Driving Car Engineer Nanodegree (the current lesson I'm on actually is a part of CS373 - so it's a kind of review lesson for me - heh). I'm having a great time learning about more in-depth understanding and knowledge relating to self-driving vehicles. Much of the learning can be applied to other areas of ML as well (learning how to use and abuse TensorFlow and Keras, for instance).

> A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37).

Don't let that stop ya! My plan after finishing this Udacity course is to actually work toward getting my BS and maybe MS in Comp Sci. By that time, I'll be well into my 44th year of age. I don't know if any of this will lead to a different direction in my career, but that isn't something I am really worried or planning about. I'm currently happy with where my career is; it pays the bills and allows for some fun, too. But if it should lead in another direction, so be it! I figure having this knowledge can't hurt me as a employment candidate, and will likely be seen as a plus. Worst case scenario, it will make my hobbyist robotics projects more interesting.

I figure I have another 20 or more years in me doing software development (assuming it remains a career option, of course); I personally have met more that a few other developers that age or older who are still making a living at it. So I'm not ruling out the possibility of a lateral move toward something involving my knowledge of machine learning.

Good luck with your studies!

The course is also quite easy to follow without buying the book. I love the exercises in which you are programming an intelligent agent to move through a maze. It reminded me of how we learned programming in university using Karel The Robot.

This alongside Andrew Ng's Machine Learning course was my first exposure to the field. https://www.coursera.org/learn/machine-learning

I can also recommend Sebastian Thrun's Artificial Ingelligence for Robotics course: https://www.udacity.com/course/artificial-intelligence-for-r...

TL;DR - read my post's "tag" and take those courses!

---

As you can see in my "tag" on my post - most of what I have learned came from these courses:

1. AI Class / ML Class (Stanford-sponsored, Fall 2011)

2. Udacity CS373 (2012) - https://www.udacity.com/course/artificial-intelligence-for-r...

3. Udacity Self-Driving Car Engineer Nanodegree (currently taking) - https://www.udacity.com/course/self-driving-car-engineer-nan...

For the first two (AI and ML Class) - these two MOOCs kicked off the founding of Udacity and Coursera (respectively). The classes are also available from each:

Udacity: Intro to AI (What was "AI Class"):

https://www.udacity.com/course/intro-to-artificial-intellige...

Coursera: Machine Learning (What was "ML Class"):

https://www.coursera.org/learn/machine-learning

Now - a few notes: For any of these, you'll want a good understanding of linear algebra (mainly matrices/vectors and the math to manipulate them), stats and probabilities, and to a lessor extent, calculus (basic info on derivatives). Khan Academy or other sources can get you there (I think Coursera and Udacity have courses for these, too - plus there are a ton of other MOOCs plus MITs Open Courseware).

Also - and this is something I haven't noted before - but the terms "Artificial Intelligence" and "Machine Learning" don't necessarily mean the same thing. Based on what I have learned, it seems like artificial intelligence mainly revolves around modern understandings of artificial neural networks and deep learning - and is a subset of machine learning. Machine learning, though, also encompasses standard "algorithmic" learning techniques, like logistic and linear regression.

The reason why neural networks is a subset of ML, is because a trained neural network ultimately implements a form of logistic (categorization, true/false, etc) or linear regression (range) - depending on how the network is set up and trained. The power of a neural network comes from not having to find all of the dependencies (iow, the "function"); instead the network learns them from the data. It ends up being a "black box" algorithm, but it allows the ability to work with datasets that are much larger and more complex than what the algorithmic approaches allow for (that said, the algorithmic approaches are useful, in that they use much less processing power and are easier to understand - no use attempting to drive a tack with a sledgehammer).

With that in mind, the sequence to learn this stuff would probably be:

1. Make sure you understand your basics: Linear Algebra, stats and probabilities, and derivatives

2. Take a course or read a book on basic machine learning techniques (linear regression, logistic regression, gradient descent, etc).

3. Delve into simple artificial neural networks (which may be a part of the machine learning curriculum): understand what feed-forward and back-prop are, how a simple network can learn logic (XOR, AND, etc), how a simple network can answer "yes/no" and/or categorical questions (basic MNIST dataset). Understand how they "learn" the various regression algorithms.

4. Jump into artificial intelligence and deep learning - implement a simple neural network library, learn tensorflow and keras, convolutional networks, and so forth...

Now - regarding self-driving vehicles - they necessarily use all of the above, and more - including more than a bit of "mechanical" techniques: Use OpenCV or another machine vision library to pick out details of the road and other objects - which might then be able to be processed by a deep learning CNN - ex: Have a system that picks out "road sign" object from a camera, then categorizes them to "read" them and use the information to make decisions on how to drive the car (come to a stop, or keep at a set speed). In essence, you've just made a portion of Tesla's vehicle assist system (first project we did in the course I am taking now was to "follow lane lines" - the main ingredient behind "lane assist" technology - used nothing but OpenCV and Python). You'll also likely learn stuff about Kalman filters, pathfinding algos, sensor fusion, SLAM, PID controllers, etc.

I can't really recommend any books to you, given my level of knowledge. I've read more than a few, but most of them would be considered "out of date". One that is still being used in university level courses is this:

http://aima.cs.berkeley.edu/

https://www.amazon.com/Artificial-Intelligence-Modern-Approa...

Note that it is a textbook, with textbook pricing...

Another one that I have heard is good for learning neural networks with is:

https://www.amazon.com/Make-Your-Own-Neural-Network/dp/15308...

There are tons of other resources online - the problem is separating the wheat from the chaff, because some of the stuff is outdated or even considered non-useful. There are many research papers out there that can be bewildering. I would say if you read them, until you know which is what, take them all with a grain of salt - research papers and web-sites alike. There's also the problem of finding diamonds in the rough (for instance, LeNet was created in the 1990s - but that was also in the middle of an AI winter, and some of the stuff written at the time isn't considered as useful today - but LeNet is a foundational work of today's ML/AI practices).

Now - history: You would do yourself good to understand the history of AI and ML, the debates, the arguments, etc. The base foundational work come from McCulloch and Pitts concept of an artificial neuron, and where that led:

https://en.wikipedia.org/wiki/Artificial_neuron

Also - Alan Turing anticipated neural networks of the kind that wasn't seen until much later:

http://www.alanturing.net/turing_archive/pages/reference%20a...

...I don't know if he was aware of McCulloch and Pitts work which came prior, as they were coming at the problem from the physiological side of things; a classic case where inter-disciplinary work might have benefitted all (?).

You might want to also look into the philosophical side of things - theory of mind stuff, and some of the "greats" there (Minsky, Searle, etc); also look into the books written and edited by Douglas Hofstadter:

https://en.wikipedia.org/wiki/G%C3%B6del,_Escher,_Bach

There's also the "lesser known" or "controversial" historical people:

* Hugo De Garis (CAM-Brain Machine)

* Igor Aleksander

* Donald Michie (MENACE)

...among others. It's interesting - De Garis was a very controversial figure, and most of his work, for whatever it is worth - has kinda been swept under the rug. He built a few computers that were FPGA based hardware neural network machines that used cellular automata a-life to "evolve" neural networks. There were only a handful of these machines made; aesthetically, their designs were as "sexy" as the old Cray computers (seriously).

Donald Michie's MENACE - interestingly enough - was a "learning computer" made of matchboxes and beads. It essentially implemented a simple neural network that learned how to play (and win at) naughts and crosses (TIC-TAC-TOE). All in a physically (by hand) manipulated "machine".

Then there is one guy, who is "reviled" in the old-school AI community on the internet (take a look at some of the old comp.ai newsgroup archives, among others). His nom-de-plume is "Mentifex" and he wrote something called "MIND.Forth" (and translated it to a ton of other languages), that he claimed was a real learning system/program/whatever. His real name is "Arthur T. Murray" - and he is widely considered to be one of the earliest "cranks" on the internet:

http://www.nothingisreal.com/mentifex_faq.html

Heck - just by posting this I might be summoning him here! Seriously - this guy gets around.

Even so - I'm of the opinion that it might be useful for people to know about him, so they don't go to far down his rabbit-hole; at the same time, I have a small feeling that there might be a gem or two hidden inside his system or elsewhere. Maybe not, but I like to keep a somewhat open mind about these kinds of things, and not just dismiss them out of hand (but I still keep in mind the opinions of those more learned and experienced than me).

EDIT: formatting

⬐ ep103

Oh man, thank you! Thank you!

For math/stats&probability in the specific problem domain of autonomous vehicles (of which self-driving cars are a subset) I'd recommend no other than Sebastian Thrun's Udacity course "Artificial Intelligence for Robotics" [https://www.udacity.com/course/artificial-intelligence-for-r...]. The content is practical, and Thrun, in my opinion, is great at breaking down complicated topics (e.g. Kalman filters) into simpler sub-components and explaining the intuition behind them. You'll get a heavy dose of math/stats&probability in the context of autonomous robots (and in certain cases, Thrun contextualizes to his work in the DARPA grand challenges while at Stanford, which laid the groundwork for his work at Google on the self driving car project [now Waymo]). I can't speak to how much the "traditional" localization, mapping, and planning techniques taught in the course crossover to deep learning approaches, but you'll no doubt get the math/stats&probability knowledge you're looking for, in the context of self-driving cars.

> As someone who's interested in taking the Udacity course, would your recommend it?

So far, yes - but that has a few caveats:

See - I have some background prior to this, and I think it biases me a bit. First, I was one of the cohort that took the Stanford-sponsored ML Class (Andrew Ng) and AI Class (Thrun/Norvig), in 2011. While I wasn't able to complete the AI Class (due to personal reasons), I did complete the ML Class.

Both of these courses are now offered by Udacity (AI Class) and Coursera (ML Class):

https://www.udacity.com/course/intro-to-artificial-intellige...

https://www.coursera.org/learn/machine-learning

If you have never done any of this before, I encourage you to look into these courses first. IIRC, they are both free and self-paced online. I honestly found the ML Class to be easier than the AI class when I took them - but that was before the founding of these two MOOC-focused companies, so the content may have changed or been made more understandable since then.

In fact, now that I think about it, I might try taking those courses again myself as a refresher!

After that (and kicking myself for dropping out of the AI Class - but I didn't have a real choice there at the time), in 2012 Udacity started, and because of (reasons...) they couldn't offer the AI Class as a course (while for some reason, Coursera could offer the ML Class - there must have been licensing issues or something) - so instead, they offered their CS373 course in 2012 (at the time, titled "How to Build Your Own Self-Driving Vehicle" or something like that - quite a lofty title):

https://www.udacity.com/course/artificial-intelligence-for-r...

I jumped at it - and completed it as well; I found it to be a great course, and while difficult, it was very enlightening on several fronts (for the first time, it clearly explained to me exactly how a Kalman filter and PID worked!).

So - I have that background, plus everything else I have read before then or since (AI/ML has been a side interest of mine since I was a child - I'm 43 now).

My suggestion if you are just starting would be to take the courses in roughly this order - and only after you are fairly comfortable with both linear algebra concepts (mainly vectors/matrices math - dot product and the like) and stats/probabilities. To a certain extent (and I have found this out with this current Udacity course), having a knowledge of some basic calculus concepts (derivatives mainly) will be of help - but so far, despite that minor handicap, I've been ok without that greater knowledge - but I do intend to learn it:

1. Coursera ML Class 2. Udacity AI Class 3. Udacity CS373 course 4. Udacity Self-Driving Car Engineer Nanodegree

> Do you think the course prepares you enough find a Self-Driving developer job?

I honestly think it will - but I also have over 25 years under my belt as a professional software developer/engineer. Ultimately, it - along with the other courses I took - will (and have) help me in having other tools and ideas to bring to bear on problems. Also - realize that this knowledge can apply to multiple domains - not just vehicles. Marketing, robotics, design - heck, you name it - all will need or do currently need people who understand machine learning techniques.

> Would you learn enough to compete/work along side people who got their Masters/PhD in Machine Learning?

I believe you could, depending on your prior background. That said, don't think that these courses could ever substitute for graduate degree in ML - but I do think they could be a great stepping stone. I am actually planning on looking into getting my BA then Masters (hopefully) in Comp Sci after completing this course. Its something I should have done long ago, but better late than never, I guess! All I currently have is an associates from a tech school (worth almost nothing), and my high school diploma - but that, plus my willingness to constantly learn and stay ahead in my skills has never let me down career-wise! So I think having this ML experience will ultimately be a plus.

Worst-case scenario: I can use what I have learned in the development of a homebrew UGV (unmanned ground vehicle) I've been working at on and off for the past few years (mostly "off" - lol).

> Appreciate your input.

No problem, I hope my thoughts help - if you have other questions, PM me...

I'll tell you how I started my journey:

I took the Stanford ML Class in 2011 taught by Andrew Ng; ultimately, Coursera was born from it, and you can still find that class in their offerings:

https://www.coursera.org/learn/machine-learning

On a similar note, Udacity sprung up from the AI Class that ran at the same time (taught by Peter Norvig and Sebastian Thrun); Udacity has since added the class to their lineup (though at the time, they had trouble doing this - and so spawned the CS373 course):

https://www.udacity.com/course/intro-to-artificial-intellige...

https://www.udacity.com/course/artificial-intelligence-for-r...

I took the CS373 course later in 2012 (I had started the AI Class, but had to drop out due to personal issues at the time).

Today I am currently taking Udacity's "Self-Driving Car Engineer" nanodegree program.

But it all started with the ML Class. Prior to that, I had played around with things on my own, but nothing really made a whole lot of sense for me, because I lacked some of the basic insights, which the ML Class course gave to me.

Primarily - and these are key (and if you don't have an idea about them, then you should study them first):

1. Machine learning uses a lot of tools based on and around probabilities and statistics.

2. Machine learning uses a good amount of linear algebra

3. Neural networks use a lot of matrix math (which is why they can be fast and scale - especially with GPUs and other multi-core systems)

4. If you want to go beyond the "black box" aspect of machine learning - brush up on your calculus (mainly derivatives).

That last one is what I am currently struggling with and working through; while the course I am taking currently isn't stressing this part, I want to know more about what is going on "under the hood" so to speak. Right now, we are neck deep into learning TensorFlow (with Python); TensorFlow actually makes things pretty simple to create neural networks, but having the understanding of how forward and back-prop works (because in the ML Class we had to implement this using Octave - we didn't use a library) has been extremely helpful.

Did I find the ML Class difficult? Yeah - I did. I hadn't touched linear algebra in 20+ years when I took the course, and I certainly hadn't any skills in probabilities (so, Kahn Academy and the like to the rescue). Even now, while things are a bit easier, I am still finding certain tasks and such challenging in this nanodegree course. But then, if you aren't challenged, you aren't learning.

⬐ Wazzymandias

Thank you for the comprehensive response! I have a lot to learn but it's exciting.

Artificial Intelligence for Robotics Programming a Robotic Car

Sebastian Thrun (former leader of Google and Stanford's autonomous driving teams that won the DARPA challenge) teaches a class focusing on the basic methods in Artificial Intelligence to support autonomous vehicles, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Programming examples and assignments apply these methods to building self-driving car like experiments.

Free course!

https://www.udacity.com/course/artificial-intelligence-for-r...

Learning from Data by Caltech Professor Yaser Abu-Mostafa : https://work.caltech.edu/telecourse.html. There was an edX version last year

MMDS: Mining Massive Datasets by Stanford professors Jure Leskovec, Anand Rajaraman,Jeff Ullman, Link: https://www.coursera.org/course/mmds

Neural Networks for Machine Learning: Geoffrey Hinton, Link: https://www.coursera.org/course/neuralnets

Artificial Intelligence for Robotics: Programming a Robotic Car, Sebastian Thrun Link: https://www.udacity.com/course/artificial-intelligence-for-r...

Intro to Artificial Intelligence, Peter Norvig & Sebastian Thrun. This was the one which started it all in 2011, joined a little late by Andrew Ng's ML course which has been mentioned already.

Intro to Artificial Intelligence link: https://www.udacity.com/course/intro-to-artificial-intellige...

⬐ alkalait

I enjoyed MMDS, but the lectures given by Ulman were painful to sit through. The man could simply not deliver a single sentence not read verbatim from a screen.

⬐ roye

same here, had to watch some of those lectures at 2x speed

⬐ Hortinstein

I took Mr. Thrun's class on Programming a Robotic Car last year for part of my OMSCS classes at GA Tech.

They should use classes like this in undergrad computer science to show why Linear Algebra will be so important and the amazing applications you can do with it. Highly recommended.

Sebastian Thrun teaches a course called AI for Robotics on Udacity: https://www.udacity.com/course/artificial-intelligence-for-r...

⬐ tgflynn

I took that class several years ago and it was definitely not about machine learning, mostly mapping/localization and route planning. It was a good class but the claim: "Learn how to program all the major systems of a robotic car" is vastly overblown. That would only be true if things like traffic sign recognition and pedestrian detection are not considered major systems of a robotic car.

Another cool resource for learning to apply and implement Kalman filters is Udacity's AI for Robotics (focused on self driving cars) course by Sebastian Thrun. Apparently Kalman filters are how Google's self driving cars predict the velocity of other cars from their position.

https://www.udacity.com/course/artificial-intelligence-for-r...

⬐ None

None

If anyone is interested in learning more, I've found a free online-course called "Programming a Robotic Car" (https://www.udacity.com/course/artificial-intelligence-for-r...)

The lecturer is the same guy behind the Stanley car that won the DARPA challenge a few years ago (https://en.wikipedia.org/wiki/DARPA_Grand_Challenge)

Start now! There are great introductions out there. Good luck, I want to get back into robotics myself.

https://www.udacity.com/course/cs373

http://see.stanford.edu/see/lecturelist.aspx?coll=86cc8662-f...

https://www.edx.org/course/autonomous-mobile-robots-ethx-amr...

https://www.edx.org/course/robot-mechanics-control-part-i-sn...

https://www.edx.org/course/autonomous-navigation-flying-robo...

https://www.edx.org/course/underactuated-robotics-mitx-6-832...

https://www.coursera.org/course/conrob

If you are missing some math to understand these, those classes are online too.

jlees has a good suggestion. I wanted to add Udacity (https://www.udacity.com/courses#!/all). For example, the course 'Artificial Intelligence for Robotics' (https://www.udacity.com/course/cs373) may be what you're looking for. I don't know your current skill level for programming nor understanding of mathematics (stats and probability) and computer science, but the course can be self-paced, which can make it great for presenting a concept and allowing you time to dive into the material, especially supporting foundational material. There are also other lower level courses. Good luck with your studies!

⬐ dashboardfront

Taking stats ATM; I've got the AI /w robots one marked down in my list of MOOCs, though I've put it at the bottom as I think I've got to cover a lot more 'fundamentals' first. Thanks for the heads up though.

Another even better guess is that the udacity self driving car course [1] will see a huge upswing in enrollment.

[1] https://www.udacity.com/course/cs373

I ran through Udacity's CS373 course first (https://www.udacity.com/course/cs373). It was neat.

Are you talking about Sebastian Thrun's AI class on Udacity [1]? I haven't yet taken it, but I have on my todo list.

[1] https://www.udacity.com/course/cs373

⬐ chattoraj

Yes, he is.

It is actually a good book and definitely worth getting,but it really should have been called Pandas for Data Analysis.

While not directly covering the python libraries mentioned such as scikits learn this is a good into to some concepts in machine learning. https://www.udacity.com/course/cs373

If anyone is interested in some of the algorithms behind this technology, then I highly recommend Sebastian Thrun's "Artificial Intelligence for Robotics" course on Udacity [1]. He makes unintuitive probabilistic methods easily understandable. I'm not aware of a better source for getting a basic understanding of complex methods like Kalman filters and particle filters [2][3]. I had never even heard of a "histogram filter" until recently reviewing this material, and it's a perfect solution for a problem that I currently have.

[1] https://www.udacity.com/course/cs373

[2] http://en.wikipedia.org/wiki/Kalman_filter

[3] http://en.wikipedia.org/wiki/Particle_filter

⬐ mej10

I was surprised by how understandable the concepts are behind this stuff. I second this recommendation. They are really cool algorithms.

For real life, there is obviously a huge engineering component, but you can definitely see how the ideas could come together to build something like the automated cars.

⬐ jmaygarden

Exactly! I linked to Wikipedia for contrast with how unintelligibly these topics are often presented for we mere laymen.

⬐ mattbradley

I would also recommend anything by Sebastian Thrun on the topic of autonomous cars. His Udacity course is a great starting point, and it inspired me to do my graduate thesis on an autonomous car simulation.

If anyone is looking to get a little deeper, check out some of his literature during his time at Stanford:

http://robots.stanford.edu/papers/junior08.pdf

http://www.lcad.inf.ufes.br/wiki/images/7/7f/2010_-_Dolgov_e...

http://ai.stanford.edu/~ddolgov/papers/dolgov_gpp_stair08.pd...

Also, if anyone is interested, my thesis: https://github.com/mattbradley/AutonomousCar