Teaching

Content and relation to ADL activities:
Introduces all the sub-problems that need to be solved to build an autonomous driving system.

Content and relation to ADL activities:
Sensor data analysis is at the core of modern self-driving. This course gives a brief overview of the basic concepts, principles and practice of data science. The main goal is to learn to plan and carry out a simple practical data science project.

Content and relation to ADL activities:
Introduces the core concepts of machine learning and data science. Allows to do a project related to AD.

Content and relation to ADL activities:
Introduces ML algorithms called neural networks (NNs). NNs are mainly used in the perception module of modular stacks for autonomous driving, but also in other modules. In end-to-end driving approaches, the entire driving stack is one big neural network. Also, there is a possibility to do a project that is related to autonomous driving.

From recognising number plates to detecting highly invasive renal masses, computer vision algorithms have become a key driving force behind many areas vital for modern society, including transportation, medicine, e-commerce, art, and entertainment. Deep neural networks are at the heart of this progress. This course will dive into the core computer vision tasks such as image classification, object detection, and segmentation from the perspective of deep neural networks. We will review the main architectures, training pipelines, and performance metrics used in these tasks. Additionally, we will explore more advanced computer vision topics, such as image generation, attention and transformers, and self-supervised and weakly supervised learning. Students will gain the practical skills necessary to complete a real-life computer vision project.

Content and relation to ADL activities:
Basics on optimization, especially in light of trajectory optimization, model predictive control from the point of view of robotics motion planning and control.

Content and relation to ADL activities:
Basics of robot motion planning, control, and state estimation. Tasks like navigating mobile robots through obstacle filled environments.

Content and relation to ADL activities:
Allows you to spend a considerable amount of time on a topic of your choice related to AD.

Content and relation to ADL activities:
Introduces using the Robot Operating System (ROS) that is also used on the ADL’s self-driving Lexus.

Content and relation to ADL activities:
Introduces mapping (SLAM) and computer vision related topics, alongside hands-on robotics

Content and relation to ADL activities:
There is a possibility to do a project related to AD.

Content and relation to ADL activities:
The use of university high performance cluster for training machine learning models and running simulations.

Content and relation to ADL activities:
Introduces basics of geoinformatics via QGIS freeware. Amongst other things, QGIS is also relevant for creating maps used by self-driving cars.

Content and relation to ADL activities:
Introduces how spatial data can be represented and treated. Spatial data in the form of maps is crucial for modular approaches in self-driving.

Content and relation to ADL activities:
Topics related to autonomous transportation, such as intelligent traffic management and intelligent infrastructure.