Analysis of experimental data on dynamics and behavior of e-scooter riders and applications to the impact of automated driving functions on urban road safety

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.