Projects per year
Abstract
One exciting challenge for future scientists and engineers
will be to contribute to the development and introduction of
self-driving cars. Under the objective of making young talent attending engineering-focused secondary schools and universities enthusiastic about this highly innovative topic, we have developed an educational platform for advanced driver assistance systems (ADASs) and autonomous driving. Our platform is designed for a miniaturized environment with low cost and scalable complexity, simultaneously providing all of the sensors required to automate a vehicle.
To make Miniaturized ADAS suitable for a wide range of students, we integrated our platform into a LEGO train as well as radio-controlled (RC) model cars and developed an assignment catalog that includes several levels of difficulty. The main focus of this catalog is to convey the basic functionality of the sensors as well as the underlying signal processing techniques in a hands-on manner. Depending on the educational level and desired learning objectives, interested lecturers have the opportunity to select arbitrary modules to build up practical courses in accordance with their preferences.
In this article, we provide insight into this multidisciplinary educational project developed together with students for students. In addition, we introduce the most important sensors currently used in ADASs and self-driving vehicles. To illustrate the broad spectrum and multitude of possibilities of this project, we further give a rough overview of the applied signal processing strategies and highlight the weaknesses and strengths of the individual sensors. To give an impression of what practical courses for university-level students based on Miniaturized ADAS might look like, we finally present an exemplary assignment catalog for a radar-based ADAS. The reason for focusing
on radar is that, to the best of our knowledge, Miniaturized
ADAS is the only educational platform supporting frequencymodulated continuous wave (FMCW) radar sensors.
Original language | English |
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Article number | 9418574 |
Pages (from-to) | 105-114 |
Number of pages | 10 |
Journal | IEEE Signal Processing Magazine |
Volume | 38 |
Issue number | 3 |
DOIs | |
Publication status | Published - May 2021 |
Fields of science
- 202036 Sensor systems
- 202 Electrical Engineering, Electronics, Information Engineering
- 202015 Electronics
- 202022 Information technology
- 202023 Integrated circuits
- 202028 Microelectronics
- 202037 Signal processing
JKU Focus areas
- Digital Transformation
Projects
- 1 Finished
-
RF-Impairments in Highly Integrated Radar Front Ends
Huemer, M. (PI)
01.07.2016 → 31.07.2020
Project: Contract research › Industry project