Hiwi – Data Analysis & Machine Learning with Hidden Markov Models in Driving Behavior (Automated Driving)

Institut

Lehrstuhl für Ergonomie (TUM-ED)

Typ

Bachelorarbeit / Semesterarbeit / Masterarbeit / HiWi-Tätigkeit /

Inhalt

experimentell / theoretisch / konstruktiv /  

Beschreibung

In the project MiRoVA, interaction behavior in mixed traffic is studied. The focus is on scenarios with automated and manually driven vehicles. We are looking for a student assistant to support data analysis.The data include driving simulator data and eye-tracking data. The goal is to analyze action sequences and interaction patterns between three actors (see figure). Methods such as Hidden Markov Model are used for modeling.

 

Tasks: 

• Model action sequences using HMM or similar methods
• Compare interactions between driver–AV and driver–driver
• Build a structured and reusable data analysis pipeline

• Visualize and document the results

   

Voraussetzungen

Requirements: 

• Degree program in Computer Science, Data Science, or a related field
• Strong and solid background in data analysis and statistics
• Experience with machine learning, especially Hidden Markov Models or other Markov models
• Advantage: experience with time series, sensor data, or driving data

 

 

Start: Immediately (16h/Week)

 

 

Möglicher Beginn

sofort

Kontakt

Tianyu Tang, M.Sc
tianyu.tangtum.de

Ausschreibung