Pedestrian collision avoidance is a relevant safety aspect for autonomous driving systems operating in urban scenarios. This paper presents a Reinforcement Learning approach to endow the resulting agent with the following two competing capabilities: managing unexpected pedestrian crossings and tracking a specific trajectory. In particular, we use the Deep Deterministic Policy Gradient, a model-free off-policy algorithm for learning continuous actions. The effectiveness of the proposed Reinforcement Learning system and the associated training approach is demonstrated by means of numerical simulations.

A Reinforcement Learning approach for pedestrian collision avoidance and trajectory tracking in autonomous driving systems

Russo L.;Terlizzi M.;Tipaldi M.;Glielmo L.
2021-01-01

Abstract

Pedestrian collision avoidance is a relevant safety aspect for autonomous driving systems operating in urban scenarios. This paper presents a Reinforcement Learning approach to endow the resulting agent with the following two competing capabilities: managing unexpected pedestrian crossings and tracking a specific trajectory. In particular, we use the Deep Deterministic Policy Gradient, a model-free off-policy algorithm for learning continuous actions. The effectiveness of the proposed Reinforcement Learning system and the associated training approach is demonstrated by means of numerical simulations.
2021
978-1-6654-3159-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/52700
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