Gesture Learning For Self-Driving Cars
Ethan Shaotran, Jonathan Cruz, Vijay Janapa Reddi
Abstract
Human-computer interaction (HCI) is crucial for safety as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand human communications on the road. In this paper, we present Gesture Learning for Advanced Driver Assistance Systems (GLADAS), a deep learning-based self-driving car hand gesture recognition system developed and evaluated using virtual simulation. We focus on gestures as they are a natural and common way for pedestrians to interact with drivers. We challenge the system to perform in typical, everyday driving interactions with humans. Our results provide a baseline performance of 94.56% accuracy and 85.91% F1 score, promising statistics that surpass human performance and motivate the need for further research into human-AV interaction.