Trajectory prediction algorithms for intention sharing in Micromobility
| Title | Trajectory prediction algorithms for intention sharing in Micromobility |
|---|---|
| Summary | Self-prediction of trajectories by Vulnerable Road Users to share their intentions with vehicles. |
| Keywords | Prediction, vehicular networks, VRU |
| TimeFrame | Fall 2024 - Spring 2025 |
| References | [[References::[1] Elfing, Johan, and Joel Pålsson. "V2X Intention Sharing for E-bikes and E-scooters: Design and implementation of a vehicular network protocol for Vulnerable Road Users intention sharing." (2024).
[2] Croall, Ruben, and Douglas Jonsson Lundqvist. "Here I go: A prediction model for e-bike and e-scooter positioning inside a CCAM environment." (2024).]] |
| Prerequisites | Matlab or Python, ML-methods, polynomial fitting, programming for embedded systems. |
| Author | |
| Supervisor | Elena Haller, Amira Soliman, Oscar Amador Molina |
| Level | Flexible |
| Status | Open |
Vehicle-to-everything (V2X) describes wireless communication between a vehicle and any entity that may affect or may be affected by, the vehicle. "Here I go" project aims to connect vulnerable road users (VRUs) to V2X services by making them active participants of Future Transport System. This bachelor thesis will be focused on refining prediction algorithms of VRUs future states in their movement trajectory adopting the pipeline presented in [1,2]. These prediction algorithms should propose the optimal shape and dimensions for the reservation area needed for VRUs on the road. The current implementation uses Least Squares (LS) method and allows for improvements, for example using Deep Neural Networks (DNNs) for timeseries data. The main purpose for this bachelor project would be then to apply various prediction algorithms e.g. weighted LS, Kalman Filter, DNNs (e.g., LSTM and GRU) and choose the optimal one for data available from both simulated scenarios and real-life testing.
This project is more software related and we would use, besides synthetic data, our own gathered data and data from publicly-available datasets.
