Research on quantum cognition in autonomous driving

A model based on quantum cognitive theory is presented, which can take irrational behaviors and marginal events into account when judging whether pedestrians are crossing the street or not and when predicting pedestrian crossing trajectory



“Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.”

Find the open access technical paper here. Published 01/2022.

Song, Q., Wang, W., Fu, W. et al. Research on quantum cognition in autonomous driving. Sci Rep 12, 300 (2022). https://doi.org/10.1038/s41598-021-04239-y.

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