Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms

Michael Shaham, Risha Ranjan, Engin Kirda, Taskin Padir

TL;DR

A testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors is introduced, and it is found that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.

Abstract

Autonomous vehicle platoons present near- and long-term opportunities to enhance operational efficiencies and save lives. The past 30 years have seen rapid development in the autonomous driving space, enabling new technologies that will alleviate the strain placed on human drivers and reduce vehicle emissions. This paper introduces a testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors. To demonstrate the testbed's utility, we evaluate three algorithms, linear feedback and two variations of distributed model predictive control, and compare their results on a typical platooning scenario where the lead vehicle tracks a reference trajectory that changes speed multiple times. We validate our algorithms in simulation to analyze the performance as the platoon size increases, and find that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.

Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms

TL;DR

A testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors is introduced, and it is found that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.

Abstract

Autonomous vehicle platoons present near- and long-term opportunities to enhance operational efficiencies and save lives. The past 30 years have seen rapid development in the autonomous driving space, enabling new technologies that will alleviate the strain placed on human drivers and reduce vehicle emissions. This paper introduces a testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors. To demonstrate the testbed's utility, we evaluate three algorithms, linear feedback and two variations of distributed model predictive control, and compare their results on a typical platooning scenario where the lead vehicle tracks a reference trajectory that changes speed multiple times. We validate our algorithms in simulation to analyze the performance as the platoon size increases, and find that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.
Paper Structure (14 sections, 1 theorem, 9 equations, 6 figures)

This paper contains 14 sections, 1 theorem, 9 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Work
  4. Problem Statement
  5. Technical Approach
  6. Hardware
  7. Control Algorithms
  8. Vehicle dynamics.
  9. Linear feedback algorithm.
  10. Distributed model predictive control algorithms.
  11. Experimental Results and Insights
  12. Hardware results
  13. Simulation results
  14. Conclusion

Key Result

theorem thmcountertheorem

Suppose a platoon uses a predecessor follower topology, and each vehicle has dynamics given by eq:hw_dyn. If each vehicle uses the DMPC controller that requires solving eq:dmpc with the cost function given by eq:2_norm_cost, then the platoon's dynamics are asymptotically stable ( i.e., eq:platoon_as

Figures (6)

  • Figure 1: One of the vehicles used in our platoon, which is a modified version of the F1Tenth vehicle.
  • Figure 2: Predecessor follower topology. The directed edge $i \to j$ indicates vehicle $j$ has access to (or receives information from) vehicle $i$. Vehicles are indexed by $0, 1, \ldots, N$.
  • Figure 3: Results depicting the four-vehicle platoon trajectories when using three different algorithms on the hardware described in \ref{['subsec:hardware']}. The columns show the performance of the squared 2-norm DMPC, 1-norm DMPC, and the linear feedback algorithms. The position of the lead vehicle was calculated by integrating its velocity, and the positions of the following vehicles was calculated using the preceding vehicle's position offset by the distance measurement from the Pozyx UWB.
  • Figure 4: A comparison of results over ten trials of each algorithm.
  • Figure 5: Results from one simulated experiment with a 100-vehicle platoon. The reference velocity trajectory the leader follows is shown as the black curve in the velocity plots (bottom row). In the position plots, the vehicle 1 trajectory is indistinguishable from the reference. Note the difference in $y$-axis scales for the bottom row of plots. We also plot the desired positions of each vehicle relative to the leader as faded curves in the top row, and this is most visible in the linear feedback case.
  • ...and 1 more figures

Theorems & Definitions (1)

  • theorem thmcountertheorem