Pedestrian intention prediction based on dynamic fuzzy automata for vehicle driving at nighttime

In this paper, we propose a novel algorithm that can predict a pedestrian’s intention using images captured by a far-infrared thermal camera mounted on a moving car at nighttime. To predict a pedestrian’s intention in consecutive sequences, we use the dynamic fuzzy automata (DFA) method, which not only provides a systemic approach for handling uncertainty but also is able to handle continuous spaces. As the spatio-temporal features, the distance between the curbs and the pedestrian and the pedestrian’s velocity and head orientation are used. In this study, we define four intention states of the pedestrian: Standing-Sidewalk (S-SW), Walking-Sidewalk (W-SW), Walking-Crossing (W-Cro), and Running-Crossing (R-Cro). In every frame, the proposed system determines the final intention of the pedestrian as ‘Stop’ if the pedestrian’s intention state is S-SW or W-SW. In contrast, the proposed system determines the final intention of a pedestrian as ‘Cross’ if the pedestrian’s intention state is W-Cro or R-Cro. A performance comparison with other related methods shows that the performance of the proposed algorithm is better than that of other related methods. The proposed algorithm was successfully applied to our dataset, which includes complex environments with many pedestrians.