Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble

TL;DR

This paper presents a cooperative system combining 3D CNNs and smart device data, using a boosted stacking ensemble to detect cyclists' starting movements accurately and robustly in real-world scenarios.

Contribution

It introduces a novel cooperative detection method integrating spatio-temporal CNN features with device data via a stacking ensemble, enhancing starting movement detection.

Findings

Achieved high accuracy in real-world cyclist starting movement detection.

Demonstrated robustness and speed of the cooperative approach.

Validated on data from 49 subjects with 84 starting motions.

Abstract

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.