  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.
