TY - JOUR A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Hubert, Andreas A1 - Sick, Bernhard A1 - Doll, Konrad T1 - Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble JF - IEEE Transactions on Intelligent Vehicles N2 - In the 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 paper, 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 three-dimensional 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. KW - Fahrerassistenzsystem KW - Fahrrad KW - Sensortechnik Y1 - 2018 VL - 3 IS - 4 SP - 534 EP - 544 ER -