Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Human-Vehicle Leader-Follower Control using Deep Learning-Driven Gesture Recognition

Version 1 : Received: 2 February 2022 / Approved: 4 February 2022 / Online: 4 February 2022 (14:30:40 CET)

How to cite: Schulte, J.; Kocherovsky, M.; Paul, N.; Pleune, M.; Chung, C. Human-Vehicle Leader-Follower Control using Deep Learning-Driven Gesture Recognition. Preprints 2022, 2022020076 (doi: 10.20944/preprints202202.0076.v1). Schulte, J.; Kocherovsky, M.; Paul, N.; Pleune, M.; Chung, C. Human-Vehicle Leader-Follower Control using Deep Learning-Driven Gesture Recognition. Preprints 2022, 2022020076 (doi: 10.20944/preprints202202.0076.v1).

Abstract

Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems. We present a system in which an autonomous vehicle — our ACTor 1 platform — can follow a human leader who controls the vehicle through gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2 electric vehicle. This work has numerous applications, such as material transport in industrial contexts.

Keywords

leader-follower; autonomous vehicles; self-driving car; machine learning; neural networks; deep learning; YOLO; posenet; pose estimation; gesture recognition

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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