Visual scene understanding for model selection in autonomous driving

It is easier to develop models for situations limited in their scope, rather than serving a wide variety of situations. If multiple such models are used, it is also important to select the correct one each time to interpret the incoming data. An autonomous system must, therefore, be able to identify in which situation or regime of conditions it is operating, to then select the appropriate model. This work is interested in exactly this regime identification and model selection. The thesis will also investigate the possibility of using intermediate steps between two models for different operating regimes. With this last objective it could help to provide reliable control models even with scarce data availability to train these models.