  In future, vehicles and other traffic participants will be interconnected and
equipped with various types of sensors, allowing for cooperation on different
levels, such as situation prediction or intention detection. In this article we
present a cooperative approach for starting movement detection of cyclists
using a boosted stacking ensemble approach realizing feature- and decision
level cooperation. We introduce a novel method based on a 3D Convolutional
Neural Network (CNN) to detect starting motions on image sequences by learning
spatio-temporal features. The CNN is complemented by a smart device based
starting movement detection originating from smart devices carried by the
cyclist. Both model outputs are combined in a stacking ensemble approach using
an extreme gradient boosting classifier resulting in a fast and yet robust
cooperative starting movement detector. We evaluate our cooperative approach on
real-world data originating from experiments with 49 test subjects consisting
of 84 starting motions.
