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Cooperative starting intention detection of cyclists based on smart devices and infrastructure

In future traffic scenarios, 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. This article presents an approach to cooperative intention detection of starting cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and the infrastructure is equipped with a wide angle stereo camera system. The approach is based on a two-stage cooperative intention detection process consisting of a movement primitive detection in the first stage, used to recognize the current movement type, and a trajectory forecast in the second stage. In the first stage, cooperation is conducted by means of a stacking ensemble on the level of detected movement primitives. These cooperatively detected movement primitives are used in the second stage in an adaptive gating function that weights between multiple specialized forecasting models. Our cooperative method yields an earlier detection of starting motions compared to a non cooperative approach while retaining robustness. Moreover, we also show that the cooperative method using multiple models is able to reduce the effective forecasting error, reaching the same performance as the system with perfect movement primitive classification.