Vehicle Teleoperation: Performance Assessment of SRPT Approach Under State Estimation Errors

TL;DR

This study evaluates the robustness of the SRPT vehicle teleoperation method under state estimation errors and environmental disturbances, demonstrating its effectiveness with minimal sensor setups in complex scenarios.

Contribution

It provides a comprehensive simulation-based assessment of SRPT's robustness and identifies a minimal sensor set necessary for effective teleoperation under challenging conditions.

Findings

SRPT performs similarly with estimated or actual states in worst-case scenarios.

A minimal sensor set (IMU, wheel speed, steer encoder) suffices for effective control.

SRPT maintains performance despite environmental disturbances and state inaccuracies.

Abstract

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.