University of Twente Student Theses
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Predicting Ego-Bicycle Trajectory : An LSTM-based Approach Using Camera and IMU
Koornstra, J.S.D. (2023) Predicting Ego-Bicycle Trajectory : An LSTM-based Approach Using Camera and IMU.
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| Abstract: | In order to make bicycles with driver assistance systems a reality, a suitable trajectory prediction must be developed. This research will investigate sensor and trajectory prediction models that are suitable for the task, and develop such a model. In addition, two datasets consisting of sensor data collected with bicyles are created to train the model. Two different types of Long Short Term Memory (LSTM) trajectory prediction models are evaluated, one using a Convolutional Neural Network LSTM hybrid architecture, and one using only LSTM by itself. The performance of these different models is then evaluated and compared. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/96462 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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