Predicting Mispredictions: A Model of Human Misjudgment About Vulnerable Road Users’ Trajectories

Abstract

This paper presents a cognitive model designed to reproduce human drivers’ errors in predicting the motion of nearby vulnerable road users. We aim to define a computational model that, given both the trajectory of the eye gaze of a human driver and the trajectory of a bicycle, can compute the probability distribution of where the human driver believes the bicycle will be in the near future. For the design and validation of the proposed cognitive model, we tested 51 subjects in immersive virtual reality scenarios. The results indicate that the proposed model can generate probability distributions of the human drivers’ beliefs about the future bicycle position that are very similar, though not statistically equivalent, to those obtained experimentally. Such models could easily be generalized to describe how drivers misjudge the motion of other road users. This may enable ADAS to evaluate and improve drivers’ situational awareness. In the future, these models could also be used by autonomous cars to evaluate situational awareness of nearby humans, enabling a safer coexistence of autonomous vehicles and vulnerable road users.