Motion Prediction Development for Urban and Racing Autonomous Driving Applications - IDP

Institut

Lehrstuhl für Fahrzeugtechnik

Typ

Semesterarbeit /

Inhalt

 

Beschreibung

To make autonomous vehicles a reality, we are developing autonomous driving software and testing it on two different research platforms EDGAR (urban AD) and TUM Autonomous Motorsport TAM (racing AD). While their application domains differ drastically – urban vs racing - the core software functionality is shared, enabling knowledge transfer between these platforms.

This IDP project is aimed to the development of a robust evaluation benchmark for motion prediction algorithms, to be used for both EDAR and TAM. With this evaluation pipeline, our current motion prediction algorithms should be benchmarked and evaluated against the current state-of-the-art.

Work Packages:

- Literature research into state-of-the-art motion prediction algorithms

- Development of a modular prediction benchmark to evaluate and compare motion prediction algorithms for EDGAR and TAM.

- Compare and evaluate our motion prediction against the Sota approach comprehensively.

Voraussetzungen

Requirements:

- Good programming skills in Python or C++

- Ideally experience with ROS2.

- High personal motivation and independent working style

- Very good language proficiency in German, English or French

Software

Python, C++, ROS2

Tags

FTM AV, FTM AV Perception, FTM Stratil, FTM Informatik

Möglicher Beginn

sofort

Kontakt

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

Ausschreibung