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SRPT vs Smith Predictor for Vehicle Teleoperation

Jai Prakash, Michele Vignati, Edoardo Sabbioni

TL;DR

Comparing the stability and performance of SRPT with Smith predictor-based approaches for direct vehicle teleoperation in challenging scenarios shows that the SRPT approach significantly improves stability and reference tracking performance, with negligible effect of network delays on path tracking.

Abstract

Vehicle teleoperation has potential applications in fallback solutions for autonomous vehicles, remote delivery services, and hazardous operations. However, network delays and limited situational awareness can compromise teleoperation performance and increase the cognitive workload of human operators. To address these issues, we previously introduced the novel successive reference pose tracking (SRPT) approach, which transmits successive reference poses to the vehicle instead of steering commands. This paper compares the stability and performance of SRPT with Smith predictor-based approaches for direct vehicle teleoperation in challenging scenarios. The Smith predictor approach is further categorized, one with Lookahead driver and second with Stanley driver. Simulations are conducted in a Simulink environment, considering variable network delays and different vehicle speeds, and include maneuvers such as tight corners, slalom, low-adhesion roads, and strong crosswinds. The results show that the SRPT approach significantly improves stability and reference tracking performance, with negligible effect of network delays on path tracking. Our findings demonstrate the effectiveness of SRPT in eliminating the detrimental effect of network delays in vehicle teleoperation.

SRPT vs Smith Predictor for Vehicle Teleoperation

TL;DR

Comparing the stability and performance of SRPT with Smith predictor-based approaches for direct vehicle teleoperation in challenging scenarios shows that the SRPT approach significantly improves stability and reference tracking performance, with negligible effect of network delays on path tracking.

Abstract

Vehicle teleoperation has potential applications in fallback solutions for autonomous vehicles, remote delivery services, and hazardous operations. However, network delays and limited situational awareness can compromise teleoperation performance and increase the cognitive workload of human operators. To address these issues, we previously introduced the novel successive reference pose tracking (SRPT) approach, which transmits successive reference poses to the vehicle instead of steering commands. This paper compares the stability and performance of SRPT with Smith predictor-based approaches for direct vehicle teleoperation in challenging scenarios. The Smith predictor approach is further categorized, one with Lookahead driver and second with Stanley driver. Simulations are conducted in a Simulink environment, considering variable network delays and different vehicle speeds, and include maneuvers such as tight corners, slalom, low-adhesion roads, and strong crosswinds. The results show that the SRPT approach significantly improves stability and reference tracking performance, with negligible effect of network delays on path tracking. Our findings demonstrate the effectiveness of SRPT in eliminating the detrimental effect of network delays in vehicle teleoperation.
Paper Structure (17 sections, 5 equations, 13 figures, 2 tables)

This paper contains 17 sections, 5 equations, 13 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Previous Work
  4. Contribution of Paper
  5. Outline of Paper
  6. Method
  7. Network delays
  8. Smith predictor with two types of driver model
  9. Lookahead driver, H$_1$
  10. Stanley lateral controller driver, H$_2$
  11. SRPT teleoperation approach with reference-pose decider driver model
  12. Reference-pose decider driver model
  13. NMPC block
  14. Simulation Platform
  15. Experimental Setup
  16. ...and 2 more sections

Figures (13)

  • Figure 1: A pictorial representation of SRPT approach for direct vehicle teleoperation. The remote vehicle receives successive reference poses as it moves forward.
  • Figure 2: Delays observed in data transmission over 4G jai2022_2.
  • Figure 3: Smith predictor schematic for vehicle teleoperation simulation. H$_1$ and H$_2$ are types of driver models considered. Unity has no role in simulation, it is just to display the manoeuvres.
  • Figure 4: (a) Look-ahead driver model control. (b) Tuning of $k_1$ for the look-ahead driver model keeping $k_2=0.9s$ constant.
  • Figure 5: Tuning of $k$ for the Stanley controller.
  • ...and 8 more figures