22 citations · 32 across the 5 of their papers we have counts for
5 papers
Learning Direct Control Policies with Flow Matching for Autonomous Driving
Marcello Ceresini, Federico Pirazzoli, Andrea Bertogalli +5
We present a flow-matching planner for autonomous driving that directly outputs actionable control trajectories defined by acceleration and curvature profiles. The model is conditi…
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…
End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning
Alessandro Paolo Capasso, Paolo Maramotti, Anthony Dell'Eva +1
Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. However, for the majority of intersections regulated by traffic lights, the problem…
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…