Pedestrian's Intention Prediction Based on Fuzzy Finite Automata and Spatial-temporal Features

Abstract

In this research, we present a novel Fuzzy Finite Automat (FFA) for predicting pedestrian's intention for advanced driver assistant system. Because dangerous pedestrians generally have a higher moving velocity and lateral moving direction than the 'standing' pedestrian as well as tracking trajectory in the time domain, we estimate the state probability of pedestrian by considering spatial domain such as pedestrian's face (looking back or not). To consider the above characteristics over temporal and spatial domain, 'distance between a pedestrian and curb', 'distance between a pedestrian and vehicle', and 'head orientation and orientation variation', and 'speed of a pedestrian' are used to generate probability density functions for the state transition value. In this paper, the four states connected with transitions of FFA are defined as Walking-SW, Standing, W-Crossing, and R-Crossing, and these states correspond to walking sidewalk, standing sidewalk, walking crossing, and running crossing, respectively. The state changes are controlled by various transition probabilities. There is no standard dataset for evaluating prediction performance using a stereo thermal camera, and we therefore created a KMU prediction dataset. The proposed algorithm was successfully applied to various pedestrian video sequences of the dataset, and showed an accurate prediction performance. © 2016 Society for Imaging Science and Technology.