Entropy-Based Intention Change Detection with a Multi-Hypotheses Filter

In the future, pedestrians and fully automated vehicles have to operate in an environment they share. To minimize the risk for pedestrians, it is very important to predict precisely their future movement. One important information source is the intention of the pedestrian. For the integration of the intention information, a Multi-Hypotheses filter is used, where different hypotheses for the intention of the pedestrian are considered. An intention change detector based on the Multi-Hypotheses filter utilizing an entropy-based confidence score is developed. With this contribution, critical real-world situations like a pedestrian crossing the street instead of following the sidewalk are tackled. The evaluation of the intention change detector is performed in simulation and for real-world data. Firstly, the proposed approach is evaluated using simulated trajectory data, where trajectories with intention changes are generated by a self-made trajectory generator (open source). Secondly, the course of the confidence score is evaluated for a real-world scenario, where the detection of the pedestrians is performed by the combination of a deep learning network (Tiny YOLO) and background subtraction. It is shown that the mean distance into the road from the sidewalk edge at the detection of the intention change is below 1.5 m, even in the case of high sensor noise. For lower sensor noise level, the intention change of the pedestrian is even detected before entering the street. Key contributions are the proposal of the Multi-Hypotheses filter, the derivation of the confidence score, the proposal of the intention detector based on the confidence score and the detection of the pedestrians and other obstacles by the fusion of background subtraction and a deep learning network.