Our work focuses on developing and evaluating enhanced inverse reinforcement learning and imitation learning methods regarding 4D trajectories, although not only for those trajectories. Thus, we follow a supervised leaning approach, and treat historical data provided by “experts” as demonstrations that a machine learning algorithm should exploit to learn the corresponding models of costs/rewards and action policies. Then, learned models can be exploited to plan or predict trajectories.
Specific Topics
- Imitation & Inverse Reinforcement Learning with Deep Machine Learning Methods
- Modelling and Doing Analytics with Trajectories in the Transportation Domain
Recent Publications
2020 |
The delta big data architecture for mobility analytics Conference 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE IEEE, Oxford, UK, 2020. |
2019 |
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods Journal Article arXiv, cs.MA , 2019. |
Journal Of Data Semantics, 8 , 2019. |
ARGO: A Big Data Framework for Online Trajectory Prediction Conference SSTD 2019, 2019. |
Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data. Journal Article GEOINFORMATICA, 23 , 2019. |