Master Thesis : Context-Aware In-Car Recommender System: Predicting UI Interactions (User Needs) with Driving Context in Weissach bei Dr Ing h c F Porsche AG

Join our expert team of engineers and researchers to work on the future of automotive user interfaces. You will take on responsibility in an exciting project from day 1 and be able to contribute your ideas while getting to know the Porsche spirit.

Motivation

For the next generation of automotive user interface (UI) systems, it will be essential to have a profound understanding of the user interface display and operating behavior. Information we can gain through sensors & external APIs are the enabler for an optimized user interface, hence a more performant driver experience. One factor is being able to predict the next UI interaction based on the driving context.

Objective

The master thesis aims for a recommender system that recognizes operating patterns for classified display & control sequences on a large naturalistic driving data set of vehicle bus information.

The exact structure of the thesis will be defined with the thesis student and the supervisor.

Tasks

• Research on the state of the art in the area of Deep Learning (LSTM, CNN architectures), Time Series Classification and Prediction, Representation Learning for detecting user interaction patterns, and Recommender Systems
• Exploratory and predictive ML model for structural user interaction pattern recognition
• Appropriate feature engineering and fusing high-dimensional multivariate time series data ranging from continuous, categorical and textual input variables
• Model evaluation regarding statistical efficiency, technological feasibility and prediction quality
• Analysis of applied algorithms under human-computer interaction aspects

Start: February/March 2023

Duration: 6 months

• Studies in engineering or natural sciences (e.g. computer science, mathematics, physics, electrical engineering, etc.), ideally with a focus on machine learning, data science, information technology, quantitative methods
• Qualified knowledge in the field of Machine Learning (e.g. with Python, CNN) and experience in dealing with large data sets
• Very good programming skills in Python
• experience with at least one deep learning framework, e.g. TensorFlow or PyTorch
• Ideally experience with implementing prototypical ML and (mobile) UI applications in practice (for example with tensorflow.js)
• Very good written and spoken English
• Passion to learn and apply new programming languages and technology concepts
• Analytical thinking, structured and independent way of working, creativity and the motivation to grow with challenges

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