Viktor Matkovic
ABSTRACT
In this paper, we present a novel approach for identifying various bike types and e-scooters using sensor readings from the cyclist’s smartphone. Bike type identification is necessary to provide context-aware navigation services that consider e-scooter- and bike-specific road conditions in route planning and improve safety and comfort by suggesting roads suitable for the cyclist’s bike type. In addition, the idea of bike type identification is useful for advertising purposes or for improving VPA (Virtual Personal Assistants) capabilities with non-intrusive, bike or e-scooter specific suggestions. We employ a CNN (Convolutional Neural Network) deep learning approach to differentiate between various bike-types and e-scooters. The evaluation includes various roads, cyclists, bike types and smartphones. The results show that bike types are identified with average F1-scores, Accuracy and AUC of up to 0.92, 0.90 and 0.98 respectively.
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