Pedestrian Trajectory Prediction Using Pre-trained Machine Learning Model for Human-Following Mobile Robot

Abstract

Until now, we have been studying a method for predicting the future trajectory of a pedestrian using a machine learning algorithm for the purpose of improving the tracking accuracy of a human-following mobile robot. Here, an open dataset was used to generate the predictor during the training phase, and the future trajectory was predicted by using just one sensor on the robot during the prediction phase. However, the specific method of constructing the training data was not considered, and there was a problem that sufficient accuracy was not obtained in the case of selecting inadequate datasets. To solve the problem, in this paper, we propose a method of extracting similar sets of data from an open dataset by expressing the features of the data in the target environment as a probability distribution and evaluating the divergence between the source distribution and the target distribution. Specifically, we express the features of a set of data as a multidimensional Gaussian distribution and compare the similarity between the distributions using the Kullback-Leibler divergence. In order to verify the effectiveness of the proposed method, we conduct an evaluation experiment using an LSTM-based prediction model as a machine learning algorithm. The results show that we can express the similarity of the movement tendency of pedestrians in the dataset by the Kullback-Leibler divergence based on the target dataset and that the prediction accuracy increases as the value is smaller.