  Vehicle teleoperation has numerous potential applications, including serving
as a backup solution for autonomous vehicles, facilitating remote delivery
services, and enabling hazardous remote operations. However, complex urban
scenarios, limited situational awareness, and network delay increase the
cognitive workload of human operators and degrade teleoperation performance. To
address this, the successive reference pose tracking (SRPT) approach was
introduced in earlier work, which transmits successive reference poses to the
remote vehicle instead of steering commands. The operator generates reference
poses online with the help of a joystick steering and an augmented display,
potentially mitigating the detrimental effects of delays. However, it is not
clear which minimal set of sensors is essential for the SRPT vehicle
teleoperation control loop.
  This paper tests the robustness of the SRPT approach in the presence of state
estimation inaccuracies, environmental disturbances, and measurement noises.
The simulation environment, implemented in Simulink, features a 14-dof vehicle
model and incorporates difficult maneuvers such as tight corners, double-lane
changes, and slalom. Environmental disturbances include low adhesion track
regions and strong cross-wind gusts. The results demonstrate that the SRPT
approach, using either estimated or actual states, performs similarly under
various worst-case scenarios, even without a position sensor requirement.
Additionally, the designed state estimator ensures sufficient performance with
just an inertial measurement unit, wheel speed encoder, and steer encoder,
constituting a minimal set of essential sensors for the SRPT vehicle
teleoperation control loop.
