Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning

Understand

Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current development in the field of intelligent vehicles.

• As especially pedestrians and cyclists are very agile and have a variety of movement options, modeling their behavior in traffic scenes is a challenging task.
• In this article we propose movement models based on machine learning methods, in particular artificial neural networks, in order to classify the current motion state and to predict the future trajectory of VRUs.
• Both model types are also combined to enable the application of specifically trained motion predictors based on a continuously updated pseudo probabilistic state classification.