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

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.