Development of a modular sensor platform for mobile detection of vehicle encounters

Riding a bike in a shared traffic area with motor vehicles causes discomfort for many bicyclists. Avoiding busy roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles on most roads. Acquiring a dataset that collects
smartphone sensor data on vehicle encounters could become the basis for a smartphone-based vehicle detector. Magnetometer and barometer readings are used as indicators of passing vehicles. In this thesis, a sensor platform is first constructed to collect smartphone and other sensor data while driving. The system is designed to be used with other sensor configurations in the future. A methodology is then presented to create a dataset of vehicle encounters based on data from a camera and a distance sensor on the sensor platform. This data set contains all important sensor data of a commercially available smartphone including the timestamp of vehicle encounters. Finally, a three-class classifier is trained and evaluated based on the data set. It is investigated which approach can provide a generalizable classifier. Approaches based on Random Forests are investigated for the classifier. The structure and parameters of a sliding window function are adjusted for feature generation.
Vehicle encounter detection achieves a F1 score of 75.0 % for oncoming and 88.4 % for overtaking vehicles. The obtained dataset can thus serve as an initial basis for smartphone-based detection of motor vehicle encounters. The developed sensor platform can also be adapted and used
for other measurement applications. The classifier for vehicle encounter detection based on smartphone sensing does not yet provide the desired results.