Control and Perception in Networked and Automated Vehicles
We offered this course for the very first time in the winter term 2019/2020. Due to lab capacity, currently we are able to host only 30 students per class. Students get six credit points for this course. This course follows the concept “Method. Application. Experiment.” The course teaches methods of networked control, which can be applied to systems consisting of multiple vehicles. The methods are applied in experiments, which results in a higher learning factor.
We designed this course for students in the master programs of computer science, automation engineering, and computational engineering science. The students do lab exercises in teams of two students from two different study programs.
There are no special or necessary requirements to take this course. We start the course with a survey, which we call diagnostic test. The goal of this test is to know the students’ pre-knowledge in various topics of the course, which allows the lecturer to adapt the content and the speed. Click here to go to the test.
This course combines theory of multi-agent decision-making with practical exercises in the Cyber-Physical Mobility Lab (CPM Lab, an open source platform for networked and autonomous vehicles).
In the theory part, we focus on distributing the control problem of multi-agent systems coupled via objective function or constraints using Distributed MPC (DMPC). Additionally, the course discusses the application in a multi-vehicle system, which prepares the participants for the practical lab work.
In the CPM Lab, the students implement controllers for vehicle platoons. The CPM Lab supports rapid functional prototyping. It consists of 20 model-scale vehicles for experiments and a simulation environment. Software developed in simulations can be seamlessly transferred to experiments without any adaptions. Additionally, experiments with the 20 vehicles can be extended by unlimited additional simulated vehicles. The CPM Lab allows researchers as well as students from different disciplines to see their ideas develop into reality. The CPM Lab is completely designed and developed by the group Cyber-Physical Mobility at RWTH Aachen University.
If you cannot access the CPM Lab physically, stay tuned to our remote-access, which is under construction and expected to go online in mid-2021.
The course covers the following topics
We recommend the following literature
R. Rajamani: Vehicle Dynamics and Control. Springer, 2012. DOI
S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2009. pdf
J. Maciejowski. Predictive Control with Constraints. Prentice Hall, 2002.
B. Alrifaee. Networked Model Predictive Control for Vehicle Collision Avoidance. 2017. pdf
Knowledge and Understanding
After successful participation in the course, the students
Skills and Competences
After successful participation in the course, students are able to independently perform the necessary steps for the successful development of control and perception algorithms in networked and automated vehicles. In doing so, they independently take into account the different aspects of the development and are able to evaluate to what extent the available approaches, methods and algorithms are applicable. They are also able to synthesize different control and perception algorithms. Furthermore, they can consider practical aspects by testing in the lab.
The lectures combine the following teaching methods
Each part of the lecture starts with mapping it to the lab architecture.
The students do lab exercises in teams of two students from two different study programs. Each team gets four complete days in the lab.
The exams are oral; 20 minutes per student. The exam starts with a 5 minutes presentation (unified template) about the lab results, following by 15 minutes questions on lecture content and lab work.
In the winter term 2019/2020, the students evaluated the lecture with the grade 1.6 (good) and the lab part with the grade 1.8 (good)
In the winter term 2019/2020, we hosted 15 computer science, 13 automation engineering, and 2 computational engineering science students. We had one no-show of a computer science student.
The attendees’ statistics are as follows
• Lecture: on average 21.5 of 30 registered students attended (72%).
• Lab: 26 of 30 registered students (87%).
• Exam: 24 of 26 lab attendees (92%).
Here are the course materials (slides, exercise sheets and the lab tasks) of the winter term 2019/2020. They are in continuous development. We will offer the course in the winter term 2020/2021 again and will publish the course materials here after accomplishment.
Use of Course Materials
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