Journal of safety research · 2024
Kinematic characterization of micro-mobility vehicles during evasive maneuvers.
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Terranova Paolo, Liu Shu-Yuan, Jain Sparsh, Engström Johan, Perez Miguel A
Аннотация
INTRODUCTION: Over the last decade, the increasing popularity of Micromobility Vehicles (MMVs) has led to profound changes in personal mobility, raising concerns about road safety and public health. Therefore, the effective characterization of their kinematic performances and safety boundaries is becoming crucial. Hence, this study aims to: (1) characterize the MMVs kinematic behaviors during emergency maneuvers; (2) examine how various power sources affect their performances; and (3) assess the suitability of a piecewise linear model for modeling their trajectories.
METHOD: We conducted a test track experiment involving 40 frequent riders performing emergency braking and swerving maneuvers on different electric MMVs, their traditional counterparts, and behaving as running pedestrians. A second experiment determined the swerving boundaries of different devices estimating their minimum radius of curvature.
RESULTS: Electric MMVs displayed superior braking capabilities compared to their traditional counterparts, while the opposite was observed in terms of swerving performances. Performances significantly varied across MMV-types, with handlebar-based devices (bicycles and scooters) consistently outperforming the handlebar-less MMVs (skateboards and onewheel). The piecewise linear models used for braking profiles well fitted most MMV trajectories, except for skateboards and pedestrians due their foot-ground interaction.
CONCLUSIONS: This research highlights the influence of MMVs-specific characteristics on their maneuverability, underscoring that steering or braking effectiveness in collisions may vary depending on device type and power source. Piecewise linear models effectively generated parameterized functions for modeling braking trajectories, despite further improvements are suggested given the inapplicability of the single brake-ramp assumption to all the MMVs.
PRACTICAL APPLICATIONS: The identified similarities and distinctions between MMVs could offer insights to traffic regulators and may assist MMV designers and manufacturers in enhancing the devices users' safety. The piecewise model results allow traffic events reconstructions and simulations, enabling intelligent driving system to predict MMV riders' evasive actions in critical situations.
METHOD: We conducted a test track experiment involving 40 frequent riders performing emergency braking and swerving maneuvers on different electric MMVs, their traditional counterparts, and behaving as running pedestrians. A second experiment determined the swerving boundaries of different devices estimating their minimum radius of curvature.
RESULTS: Electric MMVs displayed superior braking capabilities compared to their traditional counterparts, while the opposite was observed in terms of swerving performances. Performances significantly varied across MMV-types, with handlebar-based devices (bicycles and scooters) consistently outperforming the handlebar-less MMVs (skateboards and onewheel). The piecewise linear models used for braking profiles well fitted most MMV trajectories, except for skateboards and pedestrians due their foot-ground interaction.
CONCLUSIONS: This research highlights the influence of MMVs-specific characteristics on their maneuverability, underscoring that steering or braking effectiveness in collisions may vary depending on device type and power source. Piecewise linear models effectively generated parameterized functions for modeling braking trajectories, despite further improvements are suggested given the inapplicability of the single brake-ramp assumption to all the MMVs.
PRACTICAL APPLICATIONS: The identified similarities and distinctions between MMVs could offer insights to traffic regulators and may assist MMV designers and manufacturers in enhancing the devices users' safety. The piecewise model results allow traffic events reconstructions and simulations, enabling intelligent driving system to predict MMV riders' evasive actions in critical situations.