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

TL;DR

Tests the robustness of the successive reference pose tracking (SRPT) approach in the presence of state estimation inaccuracies, environmental disturbances, and measurement noises and demonstrate that the SRPT approach performs similarly under various worst-case scenarios, even without a position sensor requirement.

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.

Figures (15)

• Figure 1: Graphical depiction of the SRPT approach utilized for direct vehicle teleoperation. The remote vehicle is provided with successive reference poses as it progresses forward. The control-loop includes a state-estimation block.
• Figure 2: SRPT block diagrams. (a) Without state estimation, its efficacy is discussed in jai2022_3. (b) With state estimation, robustness assessment in presence of environmental disturbances and measurement noises is the prime focus of this work.
• Figure 3: Single-track vehicle model. Reprinted with permission from Ref. jai2022_2
• Figure 4: Block diagram used to optimally tune the prediction covariance $Q_k$, by minimizing prediction error of relative pose at $\hat{B}$ wrt pose at $\hat{A}$. The trajectory is traversed at $V_{Ref}=22 km/h$.
• Figure 5: Working principle of the human model block for the case of perfectly known states (Figure \ref{['fig:01_mpcScheme']}). Its task is to choose the future reference pose based on the received vehicle pose and look-ahead distance.