Using Eclipse CDT for the Udacity Self Driving Cars Nanodegree C++ projects

There are a number of C/C++ IDEs that can be used for working on the Udacity SDCND C++ projects. This blog post contains a few tips for using Eclipse CDT to solve the Extended Kalman Filter project.

  1. Get Eclipse CDT. Either by downloading and installing it from the Eclipse Website or by using the setup from my last post.
  2. Checkout the project from github
  3. invoke cmake ..  -DCMAKE_BUILD_TYPE=Debug invoke cmake ..  -G “Eclipse CDT4 – Unix Makefiles” (in contrast to the documentation, I compile with debug)
  4. This creates a .cproject and .project in the build directory. Copy those one level higher.
  5. Import the project as existing project in Eclipse.
  6. I usually compile / run from the command line. Open a shell directly from Eclipse
  7. To run your code from within Eclipse or to debug from within Eclipse, use the context menu.
  8. Debug mode will stop in “main()”, to disable this. Click on the settings for your debug configuration:
    2018-01-20_16h38_53Goto the debugger tab and disable the option:

An Integrated Development Environment for the SDCND

The self-driving car nanodegree from Udacity includes a number of different projects in Python and C++ in different environments (local PC, AWS if you don’t have an GPU). A common discussion topic on Slack is  the question, what everyone is using as editor / IDE.

Since I have been recently working on the topic of development environments for ML in automotive, I took the inspiration and set up and easy to install configuration for Eclipse including

  • PyDev for Python Development
  • CDT for C/C++ development
  • EGit for the integrated git access
  • AWS Eclipse integration for starting / stopping and accessing the AWS instances
  • Remote Systems Explorer etc. for easy access to AWS instances
  • Linux Tools for Docker for Docker management.

All these can be easily installed by means of the Eclipse installer (also known as “Oomph”). If you’d like to try the configuration, perform the following steps:

  1. Download the Eclipse Installer from
  2. Download the setup configuration from
  3. Start the Eclipse Installer. Choose the “Advanced Configuration” Mode in the drop-down menu on the top right.
  4. In the first diaglog, chose the C/C++ Developer entry as a base, and use “Oxygen” as product:
  5. Click next, use the “+” button at the top to add our setup file:
  6. Click next and proceed to the final dialog. The installer will download and install all the Eclipse packages.

You can now start to clone the SDCND repositories and work on your projects.


Udacity Self Driving Car Nano-Degree – is it worth the money?

The Udacity Self Driving Car Nano-Degree currently has a lot of visibility both within the automotive industry as well as with software developers who are interested to move into the industry. Udacity has provided the curriculum of the three-part nano-degree on which is useful for finding out what you will learn. But what kind of training material and support you get for your fees? Is it just Powerpoint slides or good video lectures? Are exercises just multiple choices or challenging projects? What support do you get? To give potential new students an impression, here is my take (after passing the first part) on what you get for your money:

  • The actual courses. They consist of textual documents as well as many video clips with explanations. The videos are professionally produced and generally explain things well. However, I also looked for further material on the web to maybe clarify some things by getting a different explanation or deepen topics of special interest.
  • A number of smaller (non-graded) exercises that can be solved directly in the browser. These consist of multiple choice quizzes as well as programming exercises and are useful for checking your understanding during the lectures.
  • Support through a mentor (online). I have not tried the mentor, since although I found solving the projects sometimes a bit challenging, I preferred asking on Slack (see below).
  • Several graded projects (five for the first part). Udacity provides a lot of the infrastructure for solving some of the projects:
    • Easy installation of the required Python environment through provided conda configurations.
    • AWS credits in case you need a virtual machine with a GPU to solve the projects that deal with neural networks. This is helpful if you do not have access to a GPU. If you own a PC/notebook with a decent Nvidia graphics card, this is not required – but it still a good opportunity to learn about AWS.
    • A Unity-based car driving simulator, including Python libraries to control the car from your own code. You will use that for one of the machine learning exercises to create a neural network to drive the car along the track automatically.
    • Collections of the data sets required to train your models – most of these are collections of publicly available data sets (traffic signs, car images) – but you get it all readily bundled.
    • Submitted projects are reviewed by a real human – it is not just a robot that checks that your code runs and produces the correct output. Many of the reviews I have received are really detailed and provide additional information for further research even when you pass the project on the first time. Some of the reviewers did code reviews and provided feedback on some of the APIs that had been used.
      In addition, Udacity really has quality requirements for passing, it is not possible to pass with sub-par implementations.
  • A discussion forum in the web. I mainly checked the forum for solved installation problems with some tools.
  • Several slack channels which are helpful both for discussing the projects as well as for networking. Udacity provides slack channels for the graded projects so that student can exchange ideas and questions as well as geographically grouped channels (e.g. by continent and county) so that you can find students nearby for cooperation.

All in all, I find the price justified for the value that you actually get as a student.





Setting up Tensorflow w/ GPU for the SDCND on Windows with Nvidia GPU

The Udacity Self-Driving-Car Nano Degree’s second project is an implementation of a traffic sign classifier with Tensorflow. Installation can be a bit confusing, because you could use your PC, an AWS instance, use Anaconda (as recommended) or Docker. Also, when installing, installation information is spread out over the documentation of Udacity, Tensorflow and Nvidia.

When you start reading documentation, Udacity will point you to Tensorflow, which will point you to Nvidia. So here is a short write-up on how I managed to install on a notebook with a Nvidia GPU.

Make sure to check versions before installing in the documentation of Tensorflow, because not all versions of Cuda and CudNN will work with Tensorflow.


  • Install CUDA® Toolkit 8.0
  • Install cuDNN v6 or v6.1 (must match CUDA version, may have to register for Nvidia developer program). Modify PATH variable manually to include path to cuDNN dll
  • Install Anaconda for windows
  • Install and activate environment carnd starter kit
  • At this point in time, you could run the tensorflow hello world, to see if tensorflow is correctly installed. This is without GPU support.
  • Start python Anaconda shell through Anaconda shell, not other shells (there will be an icon in your windows starter)
  • Execute pip install –ignore-installed –upgrade tensorflow-gpu

There is a debug srcipt provided by someone at Github that should help in troubleshooting your installation. I did not need it, I just took out the following lines to test in python program and in my Notebook to see if GPU support is running.

In addition, Tensorflow will print some additional information, if it is running with GPU on the command line. You can see it directly in the output or in the shell where you started the Jupyter notebook.

2017-10-06 21:22:02.967457: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 970M, pci bus id: 0000:01:00.0)


Octave cheat sheet for Coursera Machine Learning course

Switching from / to Octave when working with the Coursera machine learning course can be a bit of a hassle, since some of the Octave syntax is special. Here is my own cheat sheet to remember the most important statements required for the exercises.


  • , Column separator
  • ; Row separator


  • indexes start with one
  • slices just like in python : a(1,2:3) selects first row, cols 2 and 3

Adding ONES

Adds a column of ones on the left side

  • rows gets number of rows in X
  • ones(rows(X),1) creates a column vector of 1
  • […] combines both



Dot operator

do not print result of statement / REDUCE VERBOSE OUTPUT

Append “;” to statement




Neural Networks

Multiplication of layers

Assume calculation of layer with n nodes to m nodes, then \(\Theta \) will be a matrix with m rows and n colums. And the multiplication will be \(a^{(n+1)}=\Theta*a^{(n)}\). The final value will be \(a^{(n+1)}=sigmoid(\Theta*a^{(n)})\)


which means:

\(1/{1+e^{-z}}\) for each of the vector elements.


Ramping Up for the Udacity Self-Driving Car Nanodegree

I was having great fun with the first exercise / graded project of Udacity’s Self-Driving Car Nanodegree ( The first exercise is the implementation of a (simple) lane detection based on provided still images and video recordings from a dash cam. Having worked with image processing and Python before, I thought it was quite straightforward. It seems however, that some of the participants with less background in the technologies needed more ramp-up (there are several discussions on an Udacity provided slack channel).

Anyone being accepted for the Nanodegree program or planning to apply, might consider to read up on the following topics (no need to deep-dive, though):

Image processing

  • Of course, implementing a lane detection requires the processing of images. The basic knowledge required is to learn about the encoding of images, i.e. RGB representation as well as HSV/HSL representation (in case you want to do some extra processing)


Python is a straightforward scripting language, but it has some peculiarities, that you might not have seen before:

  • Slices : A dedicated syntax for retrieving partial lists/arrays/matrices
  • Assignments: Python supports slices and other things on the left side of an assignment operator
  • Lambda / methods as arguments: Your code will be invoked as callback by passing around methods as parameters.

Git / Github

  • Examples projects / configuration are provided by cloning a repo from Github
  • You can submit your first projects as a zip file or as submitting a link to a Github Repo

Being prepared for this topics will really make the first deadline (1 week) for project submission much easier.



Using AspectJ to analyze exceptions in production systems

For several automotive customers, we are building (Eclipse-based) systems that process engineering data – not only interactively, but in nightly jobs. From time to time, there might be a problem in test or even production systems, throwing an exception.

The default of Java is obviously only to show the stack trace. For a quick analysis, you might also want to like to know which arguments have been passed to the method that the exception was thrown in.

We are using AspectJ to “weave” relevant plugins (we use runtime weaving). The aspect looks like this:

This adds an aspect that is executed after an exception has been thrown. It will print a string representation of the method’s arguments. If the exception is propagated, the arguments of all methods up the call stack will be printed in sequence.

Chinese IT and tech podcasts

While I am working mainly in the domain of software engineering for automotive software, I try to keep up with general development in IT to see which technologies can be relevant for the toolchains and software engineering in complex technical systems and in general. A good way to do that is to listen to podcasts – and I try to combine that with keeping my Chinese language skills up to date by listening to Chinese tech podcasts. Information about the podcasts is dispersed over a few sites in the internet, so I’ll provide a list of my favourites. I am just listing the web sites without much comments, please have a look at the content yourself.


Tycho 0.24, pom less builds and Eclipse m2e maven integration

A very short technical article which might be useful in the next few days.

Tycho 0.24 has been released, and one of its most interesting features is the support for POM-less builds. The Tycho 0.24 release notes explain the use of the extension. However, there is a problem with the .mvn/extensions.xml not being found when using Eclipse variables in the “Base Directory” field of the run configuration.

I have created an m2e Bugzilla showing problem and workaround:

Update: The m2e team fixed the bug within 48 hours. Very nice. Should be in one of the next builds.

Automotive MDSD – What pickle?

Over at the modeling languages website, Scott Finnie has started a number of posts detailing his point of view on the status of model driven approaches. He concludes his first post with the statement “No wonder we’re in a pickle.”. Reading that, I myself was wondering that I don’t feel in a pickle at all.

Since the comment section of has limited formatting for a longish post, I’d like to put forward my 2c here.

Yes, model driven is not as visible and hyped as many other current technologies and, compared to the entire software industry, its percentual usage might not be impressing. But looking at my focus industry, automotive, I think we have come a long way in the last 10 years.

Model-Driven development is an important and accepted part of the automotive engineer’s toolbox. Most of the industry thinks of ML/SL and Ascet when hearing of the MD* terms. But actually there are so many other modeling techniques in use.

Architecture models are one of the strongest trends, not the least driven by the AUTOSAR standard. But even aside (or complementary to) AUTOSAR, the industry is investing in architecture models in UML or DSLs, often in connection with feature modeling and functional modeling. Code is generated from those architectural models, with the implementation being generated from ML/SL, Ascet, state machines or being written manually. Quality of the engineering data is improving noticeably through this approach.

Companies introduce their custom DSLs to model architecture, network communication, test cases, variants and generate a huge set of artefacts from that.  Models and transformations are being used to connect the AUTOSAR domain to the infotainment domain by transformations to/and from Genivi’s Franca, itself being used to generate code for the infotainment platform.

Model Driven approaches are popping up everywhere and a lot has happened in the past few years. One of the key factors for that development, in my point of view, is the availability of low-cost, accessible tooling and I would consider two tools to be most influential in this regard

  • Enterprise Architect made UML modeling available at a reasonable price, opening it up to a much wider market. That made it possible for more projects and individual to build their MDSD approach based on UML and (custom) generators.
  • Xtext made a great framework and tool for domain specific languages available at no cost to companies, projects and enthusiasts. It is always fun and amazing to come into an existing project and find what many teams have already built on Xtext. Xtext itself of course is standing on the shoulder of giants, Eclipse and the Eclipse Modeling Framework (EMF).

Maybe Eclipse Sirius will be able to do for Graphical DSLs what Xtext has done for textual modeling. 15 years ago I was working for a company that offered UML-modelling and one of the first template-based code generation technologies at a high price. The situation is completely different now – and very enjoyable.