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Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach

Lu Liu, Chi Xie, Xi Xiong

TL;DR

This work tackles highway bottlenecks in mixed traffic by modeling density evolution with a PDE–ODE framework and coordinating CAVs through a distributed, agent-by-agent truncated rollout of MPC. The method combines a dynamic horizon, sequential optimization, and a contraction-based stability analysis to deliver scalable, real-time coordination among CAVs while mitigating HDV-induced uncertainties. Theoretical guarantees (ISS) accompany empirical validation on a Shanghai bottleneck, showing significant reductions in travel and waiting times and substantial computational savings compared with centralized MPC and RL baselines. The results indicate strong potential for real-world deployment and point to future work on joint longitudinal-lateral control, robustness to uncertainties, and broader network scalability.

Abstract

The development of connected and autonomous vehicles (CAVs) offers substantial opportunities to enhance traffic efficiency. However, in mixed autonomy environments where CAVs coexist with human-driven vehicles (HDVs), achieving efficient coordination among CAVs remains challenging due to heterogeneous driving behaviors. To address this, this paper proposes a multiagent truncated rollout approach that enhances CAV speed coordination to improve highway throughput while reducing computational overhead. In this approach, a traffic density evolution equation is formulated that comprehensively accounts for the presence or absence of CAVs, and a distributed coordination control framework is established accordingly. By incorporating kinematic information from neighbor agents and employing an agent-by-agent sequential solution mechanism, our method enables explicit cooperation among CAVs. Furthermore, we introduce a truncated rollout scheme that adaptively shortens the optimization horizon based on the evaluation of control sequences. This significantly reduces the time complexity, thereby improving real-time performance and scalability. Theoretical analysis provides rigorous guarantees on the stability and performance improvement of the system. Simulations conducted on real-world bottleneck scenarios demonstrate that, in large-scale mixed traffic flows, the proposed method outperforms conventional model predictive control methods by reducing both the average travel time in the bottleneck area and overall computational time, highlighting its strong potential for practical deployment.

Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach

TL;DR

This work tackles highway bottlenecks in mixed traffic by modeling density evolution with a PDE–ODE framework and coordinating CAVs through a distributed, agent-by-agent truncated rollout of MPC. The method combines a dynamic horizon, sequential optimization, and a contraction-based stability analysis to deliver scalable, real-time coordination among CAVs while mitigating HDV-induced uncertainties. Theoretical guarantees (ISS) accompany empirical validation on a Shanghai bottleneck, showing significant reductions in travel and waiting times and substantial computational savings compared with centralized MPC and RL baselines. The results indicate strong potential for real-world deployment and point to future work on joint longitudinal-lateral control, robustness to uncertainties, and broader network scalability.

Abstract

The development of connected and autonomous vehicles (CAVs) offers substantial opportunities to enhance traffic efficiency. However, in mixed autonomy environments where CAVs coexist with human-driven vehicles (HDVs), achieving efficient coordination among CAVs remains challenging due to heterogeneous driving behaviors. To address this, this paper proposes a multiagent truncated rollout approach that enhances CAV speed coordination to improve highway throughput while reducing computational overhead. In this approach, a traffic density evolution equation is formulated that comprehensively accounts for the presence or absence of CAVs, and a distributed coordination control framework is established accordingly. By incorporating kinematic information from neighbor agents and employing an agent-by-agent sequential solution mechanism, our method enables explicit cooperation among CAVs. Furthermore, we introduce a truncated rollout scheme that adaptively shortens the optimization horizon based on the evaluation of control sequences. This significantly reduces the time complexity, thereby improving real-time performance and scalability. Theoretical analysis provides rigorous guarantees on the stability and performance improvement of the system. Simulations conducted on real-world bottleneck scenarios demonstrate that, in large-scale mixed traffic flows, the proposed method outperforms conventional model predictive control methods by reducing both the average travel time in the bottleneck area and overall computational time, highlighting its strong potential for practical deployment.

Paper Structure

This paper contains 25 sections, 7 theorems, 71 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Modeling and Formulation
  3. Modeling
  4. Problem Definition
  5. Optimal Policy with Multiagent Truncated Rollout
  6. Framework for Multiagent Rollout
  7. Multiagent Model Predictive Control
  8. Sequential Optimization with Truncated Rollout
  9. Cost Improvement Properties
  10. Stability Analysis
  11. Computational Issues in Rollout Algorithms
  12. Time Complexity Analysis
  13. Decision Order Optimization
  14. Error Bounds for the Objective Function
  15. Solution Algorithm for Multiagent Truncated Rollout
  16. ...and 10 more sections

Key Result

Proposition 1

The agent-by-agent sequential decision-making approach, which couples the control actions of other CAVs, is no worse than the parallel decision-making approach.

Figures (7)

  • Figure 1: Illustration of CAV coordination in mixed autonomy at a highway bottleneck.
  • Figure 2: The framework for multiagent truncated rollout approach.
  • Figure 3: A single bottleneck section on the Hujin Highway.
  • Figure 4: Sensitivity analysis of CAV penetration rate impact on system performance.
  • Figure 5: Sensitivity analysis of coordination zone length impact on system performance.
  • ...and 2 more figures

Theorems & Definitions (16)

  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Definition 1
  • Definition 2
  • Lemma 1
  • Definition 3
  • Lemma 2
  • proof
  • ...and 6 more