This paper proposes the usage of a bio-inspired action selection mechanism, known as multi-hypothesis sequen- tial probability ratio test (MSPRT), as a decision making tool in the field of autonomous driving. The focus is to investigate the capability of the MSPRT algorithm to effectively select the optimal action whenever the autonomous agent is required to drive the vehicle or, to infer the human driver intention when the agent is acting as an intention prediction mechanism. After a brief introduction to the agent, we present numerical simu- lations to demonstrate how simple action selection mechanisms may fail to deal with noisy measurements while the MSPRT provides the robustness needed for the agent implementation on the real vehicle.

MSPRT action selection model for bio-inspired autonomous driving and intention prediction / Donà, Riccardo; Rosati Papini, Gastone Pietro; Valenti, Giammarco. - ELETTRONICO. - (2019), pp. 11-14. ((Intervento presentato al convegno IROS 2019 tenutosi a Macao, China nel November, 2019.

MSPRT action selection model for bio-inspired autonomous driving and intention prediction

Donà, Riccardo;Rosati Papini, Gastone Pietro;Valenti, Giammarco
2019-01-01

Abstract

This paper proposes the usage of a bio-inspired action selection mechanism, known as multi-hypothesis sequen- tial probability ratio test (MSPRT), as a decision making tool in the field of autonomous driving. The focus is to investigate the capability of the MSPRT algorithm to effectively select the optimal action whenever the autonomous agent is required to drive the vehicle or, to infer the human driver intention when the agent is acting as an intention prediction mechanism. After a brief introduction to the agent, we present numerical simu- lations to demonstrate how simple action selection mechanisms may fail to deal with noisy measurements while the MSPRT provides the robustness needed for the agent implementation on the real vehicle.
IROS 2019 Proceedings - Workshop: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful?
Macao, China
Workshop organisers
Donà, Riccardo; Rosati Papini, Gastone Pietro; Valenti, Giammarco
MSPRT action selection model for bio-inspired autonomous driving and intention prediction / Donà, Riccardo; Rosati Papini, Gastone Pietro; Valenti, Giammarco. - ELETTRONICO. - (2019), pp. 11-14. ((Intervento presentato al convegno IROS 2019 tenutosi a Macao, China nel November, 2019.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/249351
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