Deep Learning: Mapshift-Prediction based on (depre- cated) map priors for online map perception (deutsch/english)

Forschungsthema/Bereich

Deep Learning in Autonomous Driving

Typ der Abschlussarbeit

Bachelor / Master

Startzeitpunkt

01.05.2025

Bewerbungsschluss

30.09.2025

Dauer der Arbeit

4 - 8 Monate

Beschreibung

Current state-of-the-art map construction methods such as MapTRv2 use sensor data (360° surround view camera setup and LiDAR) to construct high definition (HD) maps. These methods extract features from the sensor data and transform them into a Bird’s Eye View (BEV) representation and derive maps in polyline representation using transformer-based architectures. However, the quality of the predicted HD map depends on precise sensor data with correct calibration and an accurate vehicle localization. In real-world scenarios there is always some noise in the localization and sensor data so that a systematic correction is beneficial.

The goal of this thesis is to investigate the possibilities of a learned mapshift predictor. Therefore, the map construction model MapTRv2 should be extended by a second prediction head, which estimates the shift with respect to the x- and y-axis and a possible rotation ϕ of the predicted map compared to the ground truth map. For a reliable mapshift estimate also prior knowledge is needed. For that reason, using different types of prior knowledge such as information from deprecated maps or other information should be evaluated. The dataset that will be used in this thesis is Argoverse 2

Voraussetzung

Voraussetzungen an Studierende

• Knowledge in Python, PyTorch and Deep Learning
• Knowledge in Linux and Maps is a plus
• Independent working style and interest in learning new things

Studiengangsbereiche

• Ingenieurwissenschaften
  Elektrotechnik & Informationstechnik
  Informatik
  Maschinenbau
  Mechatronik & Informationstechnik
• Naturwissenschaften und Technik
  Mathematik
  Physik

Betreuung

Titel, Vorname, Name

Jonas, Merkert

Organisationseinheit

Institut für Mess- und Regelungstechnik

E-Mail Adresse

jonas.merkert@kit.edu

Link zur eigenen Homepage/Personenseite

Website

Bewerbung per E-Mail

Bewerbungsunterlagen

• Lebenslauf
• Notenauszug
• Immatrikulationsbescheinigung

E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an jonas.merkert@kit.edu


Zurück