Sensor fusion with an extended Kalman filter (Udacity Self-Driving Car Engineer Nanodegree, 2018)
This project demonstrates how to program an extended Kalman filter in C/C++. The extended Kalman filter is used to track a moving object using LIDAR and RADAR measurements. The implementation of the equations is pretty easy. My special contribution was to add debugging functionality and to ensure consistent coding style was used throughout the project.

Behavioral cloning (Udacity Self-Driving Car Engineer Nanodegree, 2018)
This project demonstrates how to use a convolutional neural network (CNN) to steer a car around a simulated track. The CNN must learn its behavior from a user driving the car around the same track in the simulator. My main findings were that it is pretty easy to have the car learn to drive safely around the test track while it is so much more complicated to have it learn to drive as smooth as a good human driver.

Traffic sign classification (Udacity Self-Driving Car Engineer Nanodegree, 2018)
This project demonstrates how to train a convolutional neural network (CNN) to classify traffic sign images. It was fun to see what the algorithm detects when used on pictures of traffic signs that it had never seen before. My special contributions were a fully parameterized approach for the definition of the CNN and exploring to use the CNN to find traffic signs of any size in any image.

Advanced lane finding (Udacity Self-Driving Car Engineer Nanodegree, 2018)
This project demonstrates how to find and mark lane lines in a video stream. My special contributions were a fully parameterized approach for the definition of the detection methods and adding extensive visual debugging information throughout the code. As a result I was able to create an algorithm that marks the lane lines in a video stream very smooth without having to spend a lot of time tweaking the parameters.

Freightliner New Cascadia (Daimler Trucks North America LLC, published 2016)
For this project I was planning and leading all the analysis work from the early shape studies to the final validation. A strong focus was put on identifying and developing several new aerodynamic features to set a new standard for fuel economy. The optimization of predictive powertrain control algorithms also contributed significantly to this goal. My special contributions were the ideal trade-off between aerodynamics, cooling performance and underhood temperatures as well as leading a team that ensured exceptional ride and handling performance.

Freightliner SuperTruck (Daimler Trucks North America LLC, published 2015)
In this project I was leading all the analysis work with a strong focus on tractor and trailer aerodynamics, fuel economy prediction including sophisticated controls as well as defining a cooling system that meets waste heat recovery requirements. I built up the industry leading team for developing aerodynamics and predicting fuel economy. My special contributions were the lower front shape as well as the shape of the main mirrors for which we applied a groundbreaking new optimization method.