22 citations · 43 across the 4 of their papers we have counts for
4 papers
Tackling Real-World Autonomous Driving using Deep Reinforcement Learning
Paolo Maramotti, Alessandro Paolo Capasso, Giulio Bacchiani +1
In the typical autonomous driving stack, planning and control systems represent two of the most crucial components in which data retrieved by sensors and processed by perception al…
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning
Alessandro Paolo Capasso, Giulio Bacchiani, Alberto Broggi
Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real…
Intelligent Roundabout Insertion using Deep Reinforcement Learning
Alessandro Paolo Capasso, Giulio Bacchiani, Daniele Molinari
An important topic in the autonomous driving research is the development of maneuver planning systems. Vehicles have to interact and negotiate with each other so that optimal choic…
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
Giulio Bacchiani, Daniele Molinari, Marco Patander
Expert human drivers perform actions relying on traffic laws and their previous experience. While traffic laws are easily embedded into an artificial brain, modeling human complex…