Pedestrian Intention Prediction for Autonomous Driving Using a Multiple Stakeholder Perspective Model

This paper proposes a multiple stakeholder perspective model (MSPM) which predicts the future pedestrian trajectory observed from vehicle's point of view. For the vehicle-pedestrian interaction, the estimation of the pedestrian's intention is a key factor. However, even if this interaction is commonly initiated by both the human (pedestrian) and the agent (driver), current research focuses on developing a neural network trained by the data from driver's perspective only. In this paper, we suggest a multiple stakeholder perspective model (MSPM) and apply this model for pedestrian intention prediction. The model combines the driver (stakeholder 1) and pedestrian (stakeholder 2) by separating the information based on the perspective. The dataset from pedestrian's perspective have been collected from the virtual reality experiment, and a network that can reflect perspectives of both pedestrian and driver is proposed. Our model achieves the best performance in the existing pedestrian intention dataset, while reducing the trajectory prediction error by average of 4.48% in the short-term (0.5s) and middle-term (1.0s) prediction, and 11.14% in the long-term prediction (1.5s) compared to the previous state-of-the-art.

I. INTRODUCTION

Global autonomous vehicles market accounted is expected to reach $615.02 billion by 2026 growing at a compound annual growth rate of 41.5% during the period [1]. Now it is inevitable to bring autonomous vehicle in our traffic system. However, bringing autonomous vehicle to our society would cause several issues, such as object recognition error in driving situation since it is controlled by an intelligent system which handles interactions among vehicles, drivers and pedestrians. Among these interaction problems, we point out that existing works do not focus on vehicle-pedestrian interaction even if it is essential when autonomous cars move away from the motorway-centered system and go to the city center where encounter with pedestrians occur frequently.