Starting Movement Detection of Cyclists Using Smart Devices
Maarten Bieshaar, Malte Depping, Jan Schneegans, Bernhard Sick

TL;DR
This paper presents a machine learning approach using smart devices to detect the starting movement of cyclists early, aiming to improve VRU safety through cooperative intention detection.
Contribution
It introduces a novel two-stage feature selection and an auxiliary class for early movement detection, enhancing robustness and reducing detection time.
Findings
Achieved 67% F1-score within 0.33 seconds after initial movement.
Devices in trouser pockets yield fewer false positives and faster detection.
Training classifiers for different device locations improves performance.
Abstract
In near future, vulnerable road users (VRUs) such as cyclists and pedestrians will be equipped with smart devices and wearables which are capable to communicate with intelligent vehicles and other traffic participants. Road users are then able to cooperate on different levels, such as in cooperative intention detection for advanced VRU protection. Smart devices can be used to detect intentions, e.g., an occluded cyclist intending to cross the road, to warn vehicles of VRUs, and prevent potential collisions. This article presents a human activity recognition approach to detect the starting movement of cyclists wearing smart devices. We propose a novel two-stage feature selection procedure using a score specialized for robust starting detection reducing the false positive detections and leading to understandable and interpretable features. The detection is modelled as a classification…
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