Opponent Prediction for Autonomous Racecars (SA/MA)
- Institute
- Professur für autonome Fahrzeugsysteme
- Type
- Semester Thesis Master's Thesis
- Content
- experimental theoretical
- Description
Welcome to the AVS (Autonomous Vehicle Systems) lab!
In our lab, we focus on autonomous driving, specifically on the development of software modules for autonomous vehicles, ethical behavior, and the practical application of algorithms in real-world scenarios using real vehicles. To achieve these goals, we have access to autonomous racing cars in 1/10 scale, full-scale autonomous racing cars, and autonomous vehicles for on-road traffic.
In my personal research, I primarily work with the 1/10 scale racing vehicles. One of my main areas of research is the transformation of the typical architecture of an autonomous vehicle with “Perception, Planning and Control” modules towards "End-to-End Learning." End-to-End Learning for autonomous vehicles refers to the vehicle learning directly from sensor data, using a neural network, how to drive safely and efficiently.
Prediction holds a critical role in the realm of autonomous race vehicles. This vital system empowers the vehicle to understand its environment by not only identifying objects but also predicting the behavior of other traffic participants. The valuable information gathered can subsequently be utilized in various modules to perform tasks such as planning a safe driving trajectory.
In the scope of this work, we aim to implement specific prediction algorithms for our racecar. These systems will be tested on a real F1TENTH vehicle to evaluate their performance in a real-world racing environment.
The work will involve the following tasks:
- Conducting a literature review on various existing prediction algorithms.
- Implementing different prediction algorithms.
- Creating a test dataset for evaluation purposes.
- Benchmarking the implemented algorithms using the created dataset and various pre-existing datasets.
- Requirements
The following section provides a list of skills that are helpful or necessary to perform the work. You do not need to possess each of the following skills, as they can all be learned during the thesis process. However, please be aware that obtaining them will require additional time-invest.
- Coding experience (Python, C++)
- Framework experience (ROS 1/2, Git)
- Knowledge in Automotive Engineering and Autonomous Driving
- Curious mindset
- Independent and organized work attitude
- Open-minded team player
In return for your time and dedication, we offer you multiple benefits for your academic, professional, and personal journey.
- Fascinating, future-oriented field of research
- Young and dynamic team
- Monthly pitch of your thesis progress to Professor Betz
- Academic and professional support beyond the thesis
- Organized and structured thesis project
- If suitable, publication as a scientific paper
- Thesis project in German or English
The thesis can be started immediately. Please send your resume, recent grade report, and a brief description of your fields of interest and why you are the perfect fit for this thesis to felix.jahncketum.de.
I am always on the lookout for motivated and committed students and look forward to receiving your application so we can work together on the mobility of tomorrow.
- Possible start
- sofort
- Contact
- Felix Jahncke