Eleonora D'Alessandro
Imitation Learning for autonomous highway merging with safety guarantees.
Rel. Andrea Giuseppe Bottino. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2020
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Abstract
Thanks to the innovations in Artificial Intelligence, the autonomous driving field experienced an enormous growth in recent years, opening the road to a new safe and efficient way of conceiving transportation. One of the most challenging aspects is designing a driverless car able to safely navigate the highway. A particularly critical maneuver is merging in the highway traffic coming from an on-ramp, which will be the focus of this thesis. The described task is an highly interactive process, that requires an advanced level of cooperation between drivers. In similar cases, machine learning techniques have demonstrated to be more efficient than manually designed rule-based approaches.
In particular, in this work we consider the Imitation Learning (IL) approach, whose objective is to learn how to perform a task by imitating the demonstrations of an expert
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