TY  - JOUR
PY  - 2022//
TI  - Autonomous human-vehicle leader-follower control using deep-learning-driven gesture recognition
JO  - Vehicles (Basel)
A1  - Schulte, Joseph
A1  - Kocherovsky, Mark
A1  - Paul, Nicholas
A1  - Pleune, Mitchell
A1  - Chung, Chan-Jin
SP  - 243
EP  - 258
VL  - 4
IS  - 1
N2  - 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 in the known literature. We present a system in which an autonomous vehicle--our ACTor 1 platform--can follow a human leader who controls the vehicle through hand-and-body 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. <br><br>RESULTS show that our modular pipeline design reliably recognizes human body language and translates the body language into LFA commands in real time. This work has numerous applications such as material transport in industrial contexts.<p />  <p>Language: en</p>
LA  - en
SN  - 2624-8921
UR  - http://dx.doi.org/10.3390/vehicles4010016
ID  - ref1
ER  -