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arxiv: 2507.03486 · v3 · pith:G52RY5CCnew · submitted 2025-07-04 · 💻 cs.DC

A Distributed Consensus Algorithm for Prioritizing Autonomous Vehicle Passing at Unsignalized Intersections under Mixed Traffic

Younjeong Lee , Young Yoon This is my paper

Pith reviewed 2026-05-22 12:40 UTC · model grok-4.3

classification 💻 cs.DC
keywords distributed consensusautonomous vehiclesunsignalized intersectionsmixed trafficpassing priorityRaft algorithmvehicle-to-vehicle communication
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The pith

A Raft-inspired voting algorithm lets CAVs agree on passing priority at unsignalized intersections in 30-40 ms even with human-driven vehicles present.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a voting-based distributed consensus method for connected autonomous vehicles to decide who goes first at intersections without traffic lights, even when ordinary cars are around. It adapts the Raft leader election idea so that the vehicles vote and pick a temporary leader to break ties when multiple arrive at once. This matters because it could let self-driving cars coordinate safely and quickly without central control, reducing delays and crashes at common intersections. Experiments show the voting finishes fast enough for real driving. A backup camera system reads license plates to set order if the network fails.

Core claim

The proposed voting-based distributed consensus algorithm, structured around the candidate and leader election processes and incorporating a minimal consensus quorum, enables CAVs to reach agreement on passing priority under asynchronous communication conditions, achieving stable consensus in mixed traffic with human-driven vehicles that lack adequate functionalities to interact with CAVs, with experimental results showing average consensus times of 30-40 ms at a typical four-way two-lane intersection.

What carries the argument

Raft-inspired candidate and leader election processes with a minimal consensus quorum for tie-breaking among simultaneously arriving CAVs.

Load-bearing premise

CAVs can perceive the entry order of surrounding vehicles using computer vision technology, are capable of avoiding collisions, and are SAE Level-4 or higher autonomous vehicles.

What would settle it

A real-world test at a four-way two-lane unsignalized intersection where the algorithm fails to reach consensus or takes substantially longer than 40 ms on average when communication delays increase or the proportion of human-driven vehicles rises.

Figures

Figures reproduced from arXiv: 2507.03486 by Young Yoon, Younjeong Lee.

Figure 1
Figure 1. Figure 1: The communication structure of AIM: (a) Centralized AIM, (b) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Intersection Consensus Flow Diagram : Vehicles entering simultane [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: 1) Initial State at Unsignalized Intersections: All vehicles entering an unsignalized intersection simultaneously start in the Init-Candidate state unlike the original Raft algorithm where all processes start in the F ollower state. Each vehicle initializes its address and direction and first casts a vote for itself. Subsequently, it simultaneously sends vote requests to all other vehicles and performs the… view at source ↗
Figure 3
Figure 3. Figure 3: The consensus process among CAVs simultaneously entering an [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of consensus timeout rates and average consensus times [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of consensus timeout rates and throughput under Full [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

We propose a methodology for connected autonomous vehicles (CAVs) to determine their passing priority at unsignalized intersections where they coexist with human-driven vehicles (HVs). Assuming that CAVs can perceive the entry order of surrounding vehicles using computer vision technology and are capable of avoiding collisions, we introduce a voting-based distributed consensus algorithm inspired by Raft to resolve tie-breaking among simultaneously arriving CAVs. The algorithm is structured around the candidate and leader election processes and incorporates a minimal consensus quorum to ensure both safety and liveness among CAVs under typical asynchronous communication conditions. Assuming CAVs to be SAE (Society of Automotive Engineers) Level-4 or higher autonomous vehicles, we implemented the proposed distributed consensus algorithm using gRPC. By adjusting variables such as the CAV-to-HV ratio, intersection scale, and the processing time of computer vision modules, we demonstrated that stable consensus can be achieved even under mixed-traffic conditions involving HVs without adequate functionalities to interact with CAVs. Experimental results show that the proposed algorithm reached consensus at a typical unsignalized four-way, two-lane intersection in approximately 30-40 ms on average. A secondary vision-based system is employed to complete the crossing priorities based on the recognized lexicographical order of the license plate numbers in case the consensus procedure times out on an unreliable vehicle-to-vehicle communication network. The significance of this study lies in its ability to improve traffic flow at unsignalized intersections by enabling rapid determination of passing priority through distributed consensus even under mixed traffic with faulty vehicles.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a voting-based distributed consensus algorithm inspired by Raft for CAVs to determine passing priority at unsignalized intersections coexisting with HVs. Assuming CAVs perceive entry order via computer vision and are SAE Level-4+, it incorporates candidate/leader election and a minimal consensus quorum for safety/liveness under asynchrony, implements the system in gRPC, and reports average consensus times of 30-40 ms via parameter sweeps on CAV-to-HV ratio, intersection scale, and vision processing time, with a secondary vision-based tiebreaker using license plate order on timeout.

Significance. If the results hold, the work could improve traffic flow at unsignalized intersections by enabling rapid distributed priority resolution even with non-communicating HVs. The gRPC implementation and parameter sweeps provide concrete, reproducible performance data supporting feasibility under mixed traffic, which is a practical strength for deployment-oriented claims.

major comments (2)
  1. [Algorithm description and quorum definition] The minimal consensus quorum (inspired by Raft and central to safety/liveness guarantees) assumes a known, fixed set of participating CAVs whose count is derived from computer-vision perception of surrounding vehicles. However, the manuscript provides no mechanism or analysis for maintaining a consistent view of the dynamic CAV population under continuous arrivals/departures or for handling perception omissions/false positives. An incorrect quorum size directly invalidates the majority guarantee and risks safety violations or liveness failures; this assumption is load-bearing for the central claim of stable consensus under mixed traffic.
  2. [Experimental results and parameter sweeps] The experimental claim of 30-40 ms average consensus time across sweeps lacks error bars, baseline comparisons to alternative priority schemes, or statistical significance tests. Post-hoc adjustments to CAV-to-HV ratio and vision processing time are mentioned but not analyzed for their impact on the performance result, weakening support for the robustness of the central timing claim.
minor comments (2)
  1. [Abstract and system overview] Clarify how the secondary vision-based system (lexicographical license plate order on consensus timeout) integrates with the primary Raft-style procedure without introducing new conflicts.
  2. [Implementation section] Add more detail on the gRPC communication model, including how asynchrony and message loss are simulated, to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and note planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Algorithm description and quorum definition] The minimal consensus quorum (inspired by Raft and central to safety/liveness guarantees) assumes a known, fixed set of participating CAVs whose count is derived from computer-vision perception of surrounding vehicles. However, the manuscript provides no mechanism or analysis for maintaining a consistent view of the dynamic CAV population under continuous arrivals/departures or for handling perception omissions/false positives. An incorrect quorum size directly invalidates the majority guarantee and risks safety violations or liveness failures; this assumption is load-bearing for the central claim of stable consensus under mixed traffic.

    Authors: The algorithm assumes that SAE Level-4+ CAVs obtain an accurate initial view of participating vehicles via computer vision at the moment consensus begins. Because the process completes in 30-40 ms on average, the set of vehicles is treated as stable for the duration of a single election round; newly arriving CAVs would detect the current state and join or start a subsequent round. We acknowledge that the manuscript does not provide explicit mechanisms or analysis for perception errors, false positives/negatives, or continuous population changes. In revision we will add a dedicated discussion of these assumptions, failure modes, and the role of the vision-based license-plate tiebreaker as a fallback, while clarifying that the quorum is computed from the perceived set at election start. revision: partial

  2. Referee: [Experimental results and parameter sweeps] The experimental claim of 30-40 ms average consensus time across sweeps lacks error bars, baseline comparisons to alternative priority schemes, or statistical significance tests. Post-hoc adjustments to CAV-to-HV ratio and vision processing time are mentioned but not analyzed for their impact on the performance result, weakening support for the robustness of the central timing claim.

    Authors: The reported 30-40 ms value is the observed average across the gRPC parameter sweeps. We agree that error bars (standard deviation) and explicit analysis of how CAV-to-HV ratio and vision processing time affect latency would improve clarity; these will be added to the revised figures and text. Direct baseline comparisons to alternative schemes (e.g., centralized control or other consensus protocols) were outside the scope of the current implementation-focused study, but we will expand the related-work discussion to contextualize the results. Statistical significance testing can be included where the data support it. revision: yes

Circularity Check

0 steps flagged

No significant circularity; experimental implementation with independent results

full rationale

The paper describes a Raft-inspired voting-based consensus algorithm implemented via gRPC, with results obtained from parameter sweeps on CAV-to-HV ratios, intersection scale, and vision processing times. No equations, fitted parameters, or derivations are presented that reduce to self-referential inputs or self-citations. The central performance claims (30-40 ms consensus) derive from direct execution measurements rather than by construction. The Raft inspiration is an external reference, and assumptions about perception and SAE Level-4 vehicles are stated explicitly without being smuggled in via prior self-work. This is a standard non-circular experimental systems paper.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

The central claim rests on domain assumptions about perfect perception and collision avoidance by CAVs plus reliable enough communication for the consensus quorum; no free parameters or invented entities are introduced beyond standard Raft mechanics.

axioms (3)
  • domain assumption CAVs can accurately perceive the entry order of surrounding vehicles using computer vision technology
    Explicitly stated as the basis for determining initial order before consensus.
  • domain assumption CAVs are capable of avoiding collisions
    Required for the safety guarantee of the consensus procedure.
  • domain assumption CAVs are SAE Level-4 or higher
    Stated to justify the assumption of full autonomy and V2V capability.

pith-pipeline@v0.9.0 · 5805 in / 1522 out tokens · 49792 ms · 2026-05-22T12:40:33.460757+00:00 · methodology

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