Background: Driving in urban traffic requires advanced cognitive skills: perceiving all relevant traffic participants, anticipating their likely trajectories, deciding which action to take, and controlling the vehicle. The underlying perceptual and cognitive processes are subject to occasional failures, which can depend in a complex way on learned heuristics and the cognitive load. Collisions between motor vehicles and vulnerable road users (VRU) in urban traffic remain frequent and have severe consequences. In this article, we study the behavior of drivers of motor vehicles turning right who are required to yield to cyclists riding straight through an intersection. A key potential error process is failure to perceive the cyclist.Methods: We conducted a trial with n = 35 subjects on our closed test track including observations of perceptual actions and gaze control, subject to variations in cognitive load and other factors. The artificial environment of a closed test track and the constraints due to ethical requirements pose challenges to the interpretation of any empirical trial. The current paper focuses on the trial design and on quantification of measurement validity.Results: Summary statistics involving trial features were assessed. Most participants reported that they performed the visual task of checking for cyclists in a manner similar to their behavior in real traffic (whether or not cyclist interactions were expected). The spatial distributions of driver glances to perceive cyclists were evaluated.Conclusion: The realism in this trial despite laboratory conditions may be attributable to ingrained skills and habits of participants. Laboratory trials can help to identify root causes of cognitive errors and ultimately guide efficient and effective deployment of bicycle safety countermeasures.

In order to facilitate safe interactions between automated vehicles (AVs) and vulnerable road users (VRUs) such as bicyclists, we present a communication application for mobile devices that allows an AV or its passenger and a bicyclist to interact in certain traffic scenarios. At the intersection, the AV or its passenger can change the existing right-of-way rules to prioritise the ego-vehicle or the bicyclist. In a coupled driving simulator in which these two road users can interact, 16 proof-of-concept experiments are conducted. It is found that the perceived safety at conflict points can be increased through the use of the application. An investigation of the user data provides insights into the AV passengers’ decision types and duration in the scenarios studied. Moreover, the simulation results are used to revise and further develop the application concept.

The introduction of micro-mobility, such as e-scooters, brings new challenges. Nevertheless, these trend devices are spreading rapidly without a comprehensive study of their interactions with other road users. For example, many countries currently require drivers of e-scooters to signal turns by hand. In this work, we investigate whether e-scooter riders can do this without losing control and whether they perceive hand signals as safe enough to use in traffic. We have conducted two studies with 10 and 24 participants, respectively. Each participant was able to perform hand signals without apparent problems. We also observed an intensive training effect regarding the handling of e-scooters. Nevertheless, our results indicate that a considerable number of inexperienced riders will, outside the laboratory, turn without signs with the currently prevailing e-scooter designs and regulations due to uncertainties.

Testing and validation of automated driving functions represent major challenges for automobile manufacturers and other stakeholders. Simulation of automated driving functions in a virtual world has the potential to accelerate testing and improve the quality of the optimization process. Among the many challenges in the urban context, a critical problem cluster involves the safety impacts of automated driving functions on vulnerable road users (VRU). Virtual assessment of safety impacts requires validated models of VRU behavior, particularly behavior related to “failure modes” and reactions in critical situations. Among VRU, there is an especially urgent need for data and models to describe the dynamics and behavior of e-scooter riders. A recent study performed by our group under laboratory conditions has provided data with implications for e-scooter stability, in particular the impact of hand signals and rear blind spot checks. It turns out that even novice e-scooter riders can successfully learn to maintain stability while performing these tasks. To understand the details of maintaining stability, a more profound understanding of the dynamical modes of the e-scooter, including the control and guidance process performed by the rider, would be of great utility, particularly to address the problem of realistic e-scooter models for simulations. To this end, more comprehensive e-scooter testing environments with enhanced sensor technology should be developed.