The role of perceptual failure and degrading processes in urban traffic accidents: a stochastic computational model for virtual experiments

Automated driving functions (ADF) are considered as a potential solver of current problems in road traffic regarding safety, efficiency and comfort. However, testing ADF by naturalistic driving in the real world is subject to technical and ethical constraints. Virtual randomized controlled trial designs potentially contribute to bypass these limitations. For this purpose, real traffic is replaced by simulated traffic, constituting the “reference” in analogy to randomized controlled trials in medicine. Specific realizations of ADF can then be integrated into the simulated traffic as a “treatment” to evaluate their efficacy. A key challenge is modelling current manual traffic, taking into account stochastic variations in the cognitive and kinematic behavior of both drivers and vulnerable road users (VRU) such as pedestrians, cyclists, or e-scooter riders. Odd sample combinations of the underlying distributions can lead to accident risk and therefore have to be modeled realistically to generate validated efficacy estimates. In particular perceptual failures and degrading of perceived stimuli are regarded causal factors for failures in traffic, which is in general remarkably safe due to multiple redundancies. Therefore a model of human information acquisition constitutes an essential ingredient to our assessment paradigm. However, complex cognitive processes play a key role, which are themselves still under scientific investigation. What we do know is that inherent limited processing abilities of humans contribute to failures in the otherwise remarkably safe traffic flow process, especially in urban areas where cognitive demand is high. We therefore restrict ourselves to model the failure and degrading processes which ultimately lead to accident risk. The computational model we propose takes the limited processing capacity of humans into account and is suitable for the stochastic simulation of traffic scenarios.