Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion
Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion
Murad Shoman 1,* , Hocine Imine 1, Kenth Johansson 2 and Viveca Wallqvist 2
Laboratoire Perceptions, Interactions, Comportements Simulations des usagers de la route et de la rue (PICS-L), Components and Systems Department (COSYS), Gustave Eiffel University, Champs sur Marne 77420, France
Division of Bioeconomy and Health, Department of Material and Surface Design, RISE Research Institutes of Sweden, Stockholm SE-114 28, Sweden
For correspondence.
Academic Editor: Haiping Du
Highlights of Vehicles 2023, 1(1), 1–16.
Received: 16 November 2022Accepted: 1 February 2023Published: 5 February 2023
In this paper, we present the instrumentation of a city bicycle with different sensors and devices in order to measure cyclists’ inputs (i.e., pedaling and steering) and the dynamical and kinematic properties of the bicycle. The instrumentation includes two tri-axial accelerometers, an Inertial Measurement Unit (IMU), GPS, a potentiometer, a laser scanner, a pedaling power meter, and speed and cadence sensors, in addition to a mobile eye tracker worn by the cyclists. After the instrumentation and adjustment of the sensors, a study was conducted in the city of Stockholm using the instrumented bicycle with the aim to evaluate cycling safety and comfort on snowy surface conditions. The outputs of this experiment will be employed further to study the interaction of cyclists with road infrastructure and other road users and their impact on cyclists’ behavior and cycling safety.
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Copyright © 2023 Shoman et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use and distribution provided that the original work is properly cited.
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Shoman, M., Imine, H., Johansson, K., & Wallqvist, V. (2023). Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion. Highlights of Vehicles, 1(1), 1–16.
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