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.
Visual Analytics of Flight Trajectories for Uncovering Decision Making Strategies Conference
SESAR Innovation Days (SID) 2018, 2018.
The datAcron Ontology for Semantic Trajectories Conference
ESWC 2017, 2017.
Taming big maritime data to support analytics Conference
IF & GIS 2017, Springer Springer, Shanghai, China, 2017.
1810 , 2017.
Visual Informatics, On Line , 2017.