Smart Device based Initial Movement Detection of Cyclists using Convolutional Neuronal Networks
This addresses safety for vulnerable road users like cyclists by enabling early warning systems for drivers, but it is incremental as it adapts existing methods to a specific domain.
The paper tackles the problem of detecting when cyclists start moving using smart device sensors, applying convolutional neural networks to classify movement transitions with a focus on enabling cooperative traffic systems.
For future traffic scenarios, we envision interconnected traffic participants, who exchange information about their current state, e.g., position, their predicted intentions, allowing to act in a cooperative manner. Vulnerable road users (VRUs), e.g., pedestrians and cyclists, will be equipped with smart device that can be used to detect their intentions and transmit these detected intention to approaching cars such that their drivers can be warned. In this article, we focus on detecting the initial movement of cyclist using smart devices. Smart devices provide the necessary sensors, namely accelerometer and gyroscope, and therefore pose an excellent instrument to detect movement transitions (e.g., waiting to moving) fast. Convolutional Neural Networks prove to be the state-of-the-art solution for many problems with an ever increasing range of applications. Therefore, we model the initial movement detection as a classification problem. In terms of Organic Computing (OC) it be seen as a step towards self-awareness and self-adaptation. We apply residual network architectures to the task of detecting the initial starting movement of cyclists.