Pedestrians and Cyclists’ Intention Estimation for the Purpose of Autonomous Drivin

Abstract

This article provides a systematic review of research articles on pedestrians and cyclists’ intention recognition to be integrated into autonomous vehicles, especially for decision making and motion planning. We firstly describe why the intention recognition of pedestrians and cyclists is suitable and necessary for autonomous vehicles and why they cannot only rely on traffic regulation laws. Then, we summarise, amongst others, the methodology and sensors used by eighteen peer-reviewed research articles published in relevant conferences and journals. We performed a systematic review of articles of the last 10 years from the following databases: IEEE Xplore, Science Direct, ACM digital library, Springer Link, MDPI and Web of Science. We observe from the collected articles that most of them are relying on several sensors, with a predominance including video. They mostly try to obtain the probability of crossing or the trajectory of the pedestrian/cyclist, mostly using a Recurrent Neural Network. In addition to their algorithmic contribution, 4 studies also provide a dataset. We conclude this article by talking about the remaining open challenges.