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Fabian Brune, "Measurement Framework for Tracking Ride Characteristics of a Bicycle in Outdoor Experiments," Bachelor Thesis, Department of Computer Science, Paderborn University (UPB), February 2020. (Advisors: Julian Heinovski and Falko Dressler; Referees: Falko Dressler and Platzner Marco)


Improving traffic security for Vulnerable Road Users (VRUs), such as cyclists, can avoid accidents and injuries, or even save lives. However, testing Advanced Driver Assistance Systems (ADASs) for bicycles in real traffic is dangerous. Hence, simulators have been developed that enable testing ADASs without putting the driver at risk. For the validation of such simulators a bicycle can be used that is capable of recording real world data in outdoor experiments. Existing instrumented bicycles lack expandability, accuracy or validation of sensors, they use only few sensors or are expensive. Within this thesis I explain my developed system, the Real World Cycling Environment (RCE), an expandable framework capable of tracking several bicycle parameters with its sensors that have been validated and/or calibrated accurately. It is based on a Raspberry Pi and can be built for about 100 Euro. An external GPS receiver and a camera are used to record GPS track and video, respectively. The developed sensors record speed, steering angle, and brake application. The speed sensor measures the rotational speed of the back wheel with nine signals per revolution and considers the tire pressure to calculate the actual circumference of the wheel for speed computation. The steering angle sensor has been validated with a testing station, specifically designed for the bicycle that forms the base of the system. It has a Mean Absolute Error (MAE) of 0.3°. An external high quality GPS device is used to record a GPS track. A comparison of recorded tracks to map data, as well as a comparison to two smartphones that recorded simultaneously, yielded good results. The brake sensors measure the brake handle application that can be related to the actual brake-caused deceleration. Overall, the Raspberry Pi’s resources are exploited to a low degree. With all sensors at their highest frequency and precision, the CPU usage (average) was below 39%, memory usage (maximum) and current power consumption (average) were below 26%. The recording software is designed extendable. Implementing additional sensors is straightforward. Furthermore, the developed software offers post processing with the data acquisition unit or with a different computer, as well as a simple review tool for trace analysis.

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Fabian Brune

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    author = {Brune, Fabian},
    doi = {10.17619/UNIPB/1-925},
    title = {{Measurement Framework for Tracking Ride Characteristics of a Bicycle in Outdoor Experiments}},
    advisor = {Heinovski, Julian and Dressler, Falko},
    institution = {Department of Computer Science},
    location = {Paderborn, Germany},
    month = {2},
    referee = {Dressler, Falko and Marco, Platzner},
    school = {Paderborn University (UPB)},
    type = {Bachelor Thesis},
    year = {2020},

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