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
2019 |
Guest Editorial: Special issue on mobility analytics for spatio-temporal and social data. Journal Article GEOINFORMATICA, 23 , 2019. |
2018 |
Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges Proceeding 2018. |
Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events Proceeding 2018. |
Springer, 10731 , 2018, ISBN: 978-3-319-73520-7. |
A Stream Reasoning System for Maritime Monitoring Proceeding 2018. |