  In the context of autonomous driving, where humans may need to take over in
the event where the computer may issue a takeover request, a key step towards
driving safety is the monitoring of the hands to ensure the driver is ready for
such a request. This work, focuses on the first step of this process, which is
to locate the hands. Such a system must work in real-time and under varying
harsh lighting conditions. This paper introduces a fast ConvNet approach, based
on the work of original work of OpenPose for full body joint estimation. The
network is modified with fewer parameters and retrained using our own day-time
naturalistic autonomous driving dataset to estimate joint and affinity heatmaps
for driver & passenger's wrist and elbows, for a total of 8 joint classes and
part affinity fields between each wrist-elbow pair. The approach runs real-time
on real-world data at 40 fps on multiple drivers and passengers. The system is
extensively evaluated both quantitatively and qualitatively, showing at least
95% detection performance on joint localization and arm-angle estimation.
