  In this endeavor, we developed a comprehensive system that processes
integrated visual features derived from video frames captured by a regular
camera, along with depth details obtained from a point cloud scanner. This
system is designed to anticipate driving actions, encompassing both vehicle
speed and steering angle. To ensure its reliability, we conducted assessments
where we juxtaposed the projected outcomes with the established norms adhered
to by skilled real-world drivers. Our evaluation outcomes indicate that the
forecasts achieve a noteworthy level of accuracy in a minimum of half the test
scenarios (ranging around 50-80%, contingent on the specific model). Notably,
the utilization of amalgamated features yielded superior performance in
comparison to using video frames in isolation, as demonstrated by most of the
cases.
