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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.
Virtual safety performance assessment for automated driving in complex urban traffic scenarios
(2019)
A coupled driving simulator to investigate the interaction between bicycles and automated vehicles
(2022)
In this research, we estimate the influence of different bicycle infrastructure on the severity of motorist-cyclist conflicts via an interconnected simulator study. Our focus is a specific conflict type: motorist is turning at an intersection and the cyclist is going straight, crossing the intersection based on previous evaluations from literature. Additionally, we reason on previously-conducted bicycle simulator studies and adapt specific methodological components of investigation area depiction in VR and scenario definition. In the end, we present four scenarios of a case study based on a signalized intersection in Ingolstadt, Germany, where we inspect motorist-cyclist conflicts in an interconnected simulator environment. After every simulator run both test subjects will be teleported to specific starting positions.