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Intelligent Transport Systems
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Scientific Theses

Topics for Scientific Theses

The Chair ITS-Sustainable Transport Logistics 4.0. offers students a pool of topics for scientific theses as bachelor's, master's or diploma theses.

 

Master thesis: Generative Modeling for Transportation Data Apply generative modeling to predict mobility patterns based on various available transportation datasets and assess quality of simulated data in the field of transportation.

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Master thesis: Lane Change Prediction Lane change prediction is an important point in the prevention of traffic accidents. The aim of this work is to develop and implement a framework for lane change prediction based on lidar, camera, IMU and GPS data.
 

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Master thesis: Deep Learning Approach for Driver Monitoring Systems. Desing a novel model to predict driver's state in conditional automated driving (SAE Level 3) based on drivers face and body images and predict if driver's are prepared to take back the control of the vehicle in emergency situations. 

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Denoising Images for Intelligent Transportation Systems Study the use of GANs to denoise images that are corrupt due to weather conditions (rain, snow, lightning)

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Master thesis: Driving Simulator Data Domain Adaptation Generalization of data collected through a driving simulator into real-world data by using domain adaptation approaches such as Generative Adversarial
Network for adapt lane change
prediction, object detection or pedestrian movement classification.

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Master thesis: Pedestrian Intention Prediction Study of Transformers to determine the pedestrian's intention to cross the road at different time intervals.

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