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.
Big Data Analytics for Time Critical Mobility Forecasting: From raw data to trajectory-oriented mobility analytics in the aviation and maritime domains Book Forthcoming
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.
arXiv, cs.MA , 2019.
Journal Of Data Semantics, 8 , 2019.
ARGO: A Big Data Framework for Online Trajectory Prediction Conference
SSTD 2019, 2019.