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Decentralized Merging Control of Connected and Automated Vehicles to Enhance Safety and Energy Efficiency using Control Barrier Functions

Shreshta Rajakumar Deshpande, Mrdjan Jankovic

TL;DR

This work addresses safe highway merging for connected and automated vehicles using a decentralized predictor-corrector control barrier function (CBF) framework. Each host vehicle computes its own action while predicting others’ actions and reconciling differences through disturbance estimates in a second-order CBF safety filter, eliminating the need for explicit merge ordering or a roadside coordinator. Compared with FIFO and a centralized CBF, the proposed DPC-CBF achieves significant system-wide energy efficiency gains and improved congestion metrics, while showing strong robustness to unexpected behavior such as power loss. The approach leverages standard 10 Hz V2V Basic Safety Messages and integrates with existing longitudinal controllers, offering a practical, scalable path toward safer and more efficient mixed-traffic merging.

Abstract

This paper presents a decentralized Control Barrier Function (CBF) based approach for highway merging of Connected and Automated Vehicles (CAVs). In this control algorithm, each "host" vehicle negotiates with other agents in a control zone of the highway network, and enacts its own action, to perform safe and energy-efficient merge maneuvers. It uses predictor-corrector loops within the robust CBF setting for negotiation and to reconcile disagreements that may arise. There is no explicit order of vehicles and no priority. A notable feature is absence of gridlocks due to instability of the inter-agent system. Results from Monte Carlo simulations show significant improvement in the system-wide energy efficiency and traffic flow compared to a first-in-first-out approach, as well as enhanced robustness of the proposed decentralized controller compared to its centralized counterpart.

Decentralized Merging Control of Connected and Automated Vehicles to Enhance Safety and Energy Efficiency using Control Barrier Functions

TL;DR

This work addresses safe highway merging for connected and automated vehicles using a decentralized predictor-corrector control barrier function (CBF) framework. Each host vehicle computes its own action while predicting others’ actions and reconciling differences through disturbance estimates in a second-order CBF safety filter, eliminating the need for explicit merge ordering or a roadside coordinator. Compared with FIFO and a centralized CBF, the proposed DPC-CBF achieves significant system-wide energy efficiency gains and improved congestion metrics, while showing strong robustness to unexpected behavior such as power loss. The approach leverages standard 10 Hz V2V Basic Safety Messages and integrates with existing longitudinal controllers, offering a practical, scalable path toward safer and more efficient mixed-traffic merging.

Abstract

This paper presents a decentralized Control Barrier Function (CBF) based approach for highway merging of Connected and Automated Vehicles (CAVs). In this control algorithm, each "host" vehicle negotiates with other agents in a control zone of the highway network, and enacts its own action, to perform safe and energy-efficient merge maneuvers. It uses predictor-corrector loops within the robust CBF setting for negotiation and to reconcile disagreements that may arise. There is no explicit order of vehicles and no priority. A notable feature is absence of gridlocks due to instability of the inter-agent system. Results from Monte Carlo simulations show significant improvement in the system-wide energy efficiency and traffic flow compared to a first-in-first-out approach, as well as enhanced robustness of the proposed decentralized controller compared to its centralized counterpart.

Paper Structure

This paper contains 19 sections, 17 equations, 5 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. Assumptions and Requirements
  3. PRELIMINARIES: CONTROL BARRIER FUNCTIONS
  4. PROBLEM FORMULATION
  5. Modeling and Second-Order Barrier Constraint Formulation
  6. DECENTRALIZED CBF MERGING CONTROL ALGORITHM
  7. Four-Vehicle Simulation: An Example
  8. Tuning the Parameters
  9. EVALUATION AND RESULTS
  10. Benchmark Controllers
  11. FIFO Control
  12. Centralized-CBF Merging Control
  13. Monte Carlo Simulation Setup
  14. Robustness to Unexpected Behavior (Power Loss Due to Malfunction)
  15. Metrics for Merging Energy and Time Efficiency Evaluation
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Highway merging scenario with communicating CAVs.
  • Figure 2: Simulation with four agents showing velocity, acceleration and disturbance estimate traces.
  • Figure 3: Robustness to unexpected behavior: Comparison of minimum barrier distance.
  • Figure 4: Collision avoidance using DPC-CBF, verified using $h0_{min}$.
  • Figure 5: Histograms from Monte Carlo evaluation.

Theorems & Definitions (1)

  • Definition 1