Hands-On Deep Learning: Optimization of Multi-Class Motion Prediction for Autonomous Driving

Institut

Lehrstuhl für Fahrzeugtechnik

Typ

Semesterarbeit / Masterarbeit /

Inhalt

 

Beschreibung

Motion prediction of surrounding objects is a key requirement for autonomous driving. However, state-of-the-art approaches face a fundamental challenge: the motion patterns of different classes diverge strongly. Pedestrians may stop abruptly, cyclists combine pedestrian-like agility with vehicle-like dynamics, and vehicles typically follow traffic rules but can still vary widely in speed and maneuvers. Capturing all these behaviors within a single model is highly demanding, as class-specific nuances are often averaged out.

In this thesis, an existing joint tracking and prediction approach is to be further optimized to enable robust and accurate multi-class motion prediction. To achieve this, literature research will be conducted, and the existing model will be refined, with a particular focus on exploring class-specific prediction heads as a promising strategy for improving prediction performance.

Possibility of a publication in case of excellent work.

Voraussetzungen

Requirements:

• Very good programming skills in Python.

• High personal motivation and independent working style.

• Very good language proficiency in German, English or French.

Software

Python

Tags

FTM Studienarbeit, FTM AV, FTM AV Perception, FTM Stratil, FTM Informatik, FTM IDP

Möglicher Beginn

sofort

Kontakt

Loïc Stratil, M.Sc.
Raum: MW 3508
Tel.: +49.89.289.15898
loic.stratiltum.de

Ausschreibung