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Nowadays electric scooters are adopted as an eco-friendly mode of transportation worldwide. This increase in usage of e-scooters have critical impact on traffic safety. Currently many researches are done regarding traffic safety and researchers are trying to simulate the actual traffic. This has led to an increased demand for accurate e-scooter trajectory data. However, verifying the reliability and accuracy of e-scooter trajectory data creates a significant challenge due to the absence of a standardized reference vehicle and ground truth data. The focus of this thesis is to address this gap by proposing the design, implementation, and application of an e-scooter reference vehicle to record ground truth data.
The research begins by examining existing methods for recording the trajectory data and find their limitations and identifying the absence of ground truth data. To overcome these challenges, a novel e-scooter reference vehicle is developed, integrating various sensors, such as inertial measurement unit (IMU) and high precision GNSS, to capture accurate trajectory information. The implementation phase focuses on the construction and calibration of the e-scooter reference vehicle. Special attention is given to sensor integration, programming, and calibration techniques to ensure accurate and synchronized data collection. The developed system is tested in various real-world scenarios to evaluate its performance and reliability. An experiment was conducted in which the reference vehicle was driven through an inner-city intersection.
This intersection is equipped with an installed bird’s eye view camera that can generate trajectory data of road users. The generated trajectory data from the camera was compared with the ground truth data from the test vehicle to find anomalies and inaccuracies. In conclusion, the results of this research have a contribution on micro-mobility research and in this context on the development and improvement of traffic safety, urban planning and traffic research.