Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent. The safe speed function acts as a behavioral directive for the agent, thus extending its cognitive abilities. We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian's context onto the appropriate traveling speed so that the autonomous vehicle can successfully perform emergency braking maneuvers. We discuss the advantages of developing a specialized neural network extension on top of an already functioning autonomous driving system, removing the burden of learning to drive from scratch while focusing on learning safe behavior at a high-level. We demonstrate how the safe speed function can be learned in simulation and then transferred into a real vehicle. We include a statistical analysis of the network's improvements compared to the original autonomous driving system. The code implementing the presented network is available at https://github.com/tonegas/safe-speed-neural-network with MIT license and at https://zenodo.org/communities/dreams4cars.

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians / Rosati Papini, Gastone Pietro; Plebe, Alice; Da Lio, Mauro; Donà, Riccardo. - In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. - ISSN 1524-9050. - ELETTRONICO. - 2021:(2021), pp. 1-18. [10.1109/TITS.2021.3086397]

A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians

Rosati Papini, Gastone Pietro;Plebe, Alice;Da Lio, Mauro;Donà, Riccardo
2021

Abstract

Driving requires the ability to handle unpredictable situations. Since it is not always possible to predict an impending danger, a good driver should preventively assess whether a situation has risks and adopt a safe behavior. Considering, in particular, the possibility of a pedestrian suddenly crossing the road, a prudent driver should limit the traveling speed. We present a work exploiting reinforcement learning to learn a function that specifies the safe speed limit for a given artificial driver agent. The safe speed function acts as a behavioral directive for the agent, thus extending its cognitive abilities. We consider scenarios where the vehicle interacts with a distracted pedestrian that might cross the road in hard-to-predict ways and propose a neural network mapping the pedestrian's context onto the appropriate traveling speed so that the autonomous vehicle can successfully perform emergency braking maneuvers. We discuss the advantages of developing a specialized neural network extension on top of an already functioning autonomous driving system, removing the burden of learning to drive from scratch while focusing on learning safe behavior at a high-level. We demonstrate how the safe speed function can be learned in simulation and then transferred into a real vehicle. We include a statistical analysis of the network's improvements compared to the original autonomous driving system. The code implementing the presented network is available at https://github.com/tonegas/safe-speed-neural-network with MIT license and at https://zenodo.org/communities/dreams4cars.
Rosati Papini, Gastone Pietro; Plebe, Alice; Da Lio, Mauro; Donà, Riccardo
A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians / Rosati Papini, Gastone Pietro; Plebe, Alice; Da Lio, Mauro; Donà, Riccardo. - In: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. - ISSN 1524-9050. - ELETTRONICO. - 2021:(2021), pp. 1-18. [10.1109/TITS.2021.3086397]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/319597
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