Montag, 21. Juli 2025
AI for Automated Driving: End-to-End Driving Models meet Connectivity
Topic and Goal of the Thesis
Artificial Intelligence plays an increasing role in automated driving systems. Big players like Tesla, Wayve, Waymo and others are already exploring the option to replace almost the complete driving stack with one neural network, a so-called end-to-end driving model.
This thesis aims to bring these models to the next level. It explores their potential to take as input additional data shared by other vehicles and/or smart roadside infrastructure (V2X data). Connectivity allows these E2E models to make better decisions, hence making traffic safer.
Working Points
- Research of relevant literature
- Assessment of available E2E models and training pipelines
- Extension of E2E models with the ability to process V2X data
- Training of E2E models
- Open-loop and closed-loop evaluation
- Optional: Demonstration in our real-world vehicle
Requirements
- Reliability, commitment and enjoyment of working independently
- Experience with Python or C++
- Experience with the following is beneficial: ROS, Docker und Machine Learning
Hinweis: Bitte kurzen Lebenslauf und eine Notenübersicht anhängen.
Kontakt
Silas Damaschke M.Sc.
+49 241 80-26713
E-Mail
Art der Arbeit
Masterarbeit
Beginn
Earliest possible date
Vorkenntnisse
Python or C++
Sprache
Deutsch, Englisch
Forschungsbereich
Fahrzeugintelligenz & Automatisiertes Fahren
Service
Kooperationen
Adresse
Institut für Kraftfahrzeuge
RWTH Aachen University
Steinbachstraße 7
52074 Aachen · Deutschland