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

TL;DR

This paper introduces a multiagent truncated rollout approach for mixed autonomy highway traffic, improving flow efficiency and computational performance by coordinating CAVs with reduced optimization horizons.

Contribution

It proposes a novel multiagent truncated rollout method that enhances CAV coordination in mixed traffic, with adaptive horizon shortening and theoretical guarantees.

Findings

Reduces average travel time in bottlenecks

Decreases computational time significantly

Outperforms traditional MPC in simulations

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.