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Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification

Eunhyek Joa, Eric Yongkeun Choi, Francesco Borrelli

TL;DR

The method demonstrates 12% improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.

Abstract

This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the given state, and the constraints ensure that the controlled vehicle passes the upcoming traffic light timely while obeying traffic laws. The resulting convex optimization problem has a short horizon and is amenable for real-time implementations. We demonstrate the effectiveness of our approach through real-world vehicle experiments. Our method demonstrates $12\%$ improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.

Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification

TL;DR

The method demonstrates 12% improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.

Abstract

This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the given state, and the constraints ensure that the controlled vehicle passes the upcoming traffic light timely while obeying traffic laws. The resulting convex optimization problem has a short horizon and is amenable for real-time implementations. We demonstrate the effectiveness of our approach through real-world vehicle experiments. Our method demonstrates improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.
Paper Structure (23 sections, 2 theorems, 27 equations, 7 figures, 3 tables, 2 algorithms)

This paper contains 23 sections, 2 theorems, 27 equations, 7 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Setup
  3. Route segmentation
  4. Vehicle Model, Measurement Model, and Observer
  5. Energy Consumption Stage Cost
  6. Constraints
  7. Eco-Driving Problem for Connected Vehicles under Localization Uncertainty
  8. Solution approach to the problem
  9. Method
  10. Data: State-Input pairs
  11. Learning the terminal constraint
  12. Learning the terminal cost $V(\cdot)$
  13. Model Predictive Control for Eco-driving
  14. Implementation
  15. Validation
  16. ...and 8 more sections

Key Result

Proposition 1

Let $\Delta s_k = s_{k} -\hat{s}_{k}$. Then, $\Delta s_k \in \mathcal{W}$ and $n_k \in 2L\mathcal{W}$ for all realizations of noise that satisfies eq: gps error.

Figures (7)

  • Figure 1: The given route is a series of multiple, parameterized road segments.
  • Figure 2: Comparison: Vehicle energy measurement data and Simulated energy consumption
  • Figure 3: Defintion of $t_\text{red}$ and $t_\text{green}$
  • Figure 4: Illustration of the terminal constraints $\mathcal{S}_{t_\text{red}}$ and $\mathcal{P}_{t_\text{green}}$: the constraint $\hat{\mathbf{x}}_{N|k} \in \mathcal{S}_{t_\text{red}}$ is to ensure that the vehicle stays behind the traffic light until $t_\text{red}+N$ steps, while the constraint $\hat{\mathbf{x}}_{N|k} \in \mathcal{P}_{t_\text{green}}$ is to ensure that the vehicle passes the traffic light within $t_\text{green}+N$ steps.
  • Figure 5: Total Energy Consumption Improvement with Increasing Data Size. $100$ Monte Carlo simulations for each task iteration.
  • ...and 2 more figures

Theorems & Definitions (6)

  • Proposition 1
  • proof
  • Remark 1
  • Remark 2
  • Theorem 1
  • proof