GLADAS: Gesture Learning for Advanced Driver Assistance Systems
This addresses safety in human-AV interaction for autonomous vehicles, but is incremental as it builds on existing gesture recognition methods.
The paper tackles the problem of autonomous vehicles understanding pedestrian hand gestures by presenting GLADAS, a simulator-based platform for training and testing gesture recognition systems, achieving 85.91% accuracy in gesture understanding.
Human-computer interaction (HCI) is crucial for the safety of lives as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand humans on the road. In this paper, we present GLADAS, a simulator-based research platform designed to teach AVs to understand pedestrian hand gestures. GLADAS supports the training, testing, and validation of deep learning-based self-driving car gesture recognition systems. We focus on gestures as they are a primordial (i.e, natural and common) way to interact with cars. To the best of our knowledge, GLADAS is the first system of its kind designed to provide an infrastructure for further research into human-AV interaction. We also develop a hand gesture recognition algorithm for self-driving cars, using GLADAS to evaluate its performance. Our results show that an AV understands human gestures 85.91% of the time, reinforcing the need for further research into human-AV interaction.