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arxiv: 2606.24203 · v1 · pith:OZFP4IXOnew · submitted 2026-06-23 · 💻 cs.NI · cs.RO

Importance of Intent-Sharing for V2X-based Maneuver Coordination

Rafael Molina-Masegosa , Sergei S. Avedisov , Miguel Sepulcre , Javier Gozalvez , Yashar Z. Farid , Onur Altintas This is my paper

Pith reviewed 2026-06-25 22:14 UTC · model grok-4.3

classification 💻 cs.NI cs.RO
keywords intent sharingmaneuver coordinationV2Xconnected automated vehicleslane changetrajectory predictionhighway scenario
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The pith

Direct intent sharing substantially improves successful maneuver coordinations compared to kinematic prediction alone.

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

The paper sets out to show that connected automated vehicles achieve more successful coordinated maneuvers when they receive other vehicles' driving plans directly instead of inferring trajectories from current position and speed data. It focuses on lane-change coordination in highway settings and measures the fraction of attempts that succeed under each approach. A sympathetic reader would care because reliable coordination matters for preventing conflicts and enabling smoother traffic with automated vehicles on the road. The work finds clear gains from intent sharing in the two scenarios examined and concludes that maneuver coordination protocols should incorporate intent transmission.

Core claim

Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles' driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.

What carries the argument

The direct comparison of intent-sharing (remote vehicles communicate their plans) against trajectory prediction based solely on current kinematic data, applied to coordinated lane changes.

If this is right

  • Higher percentages of successful lane-change coordinations occur when plans are shared directly.
  • Maneuver coordination effectiveness rises in highway scenarios once vehicles know remote driving intentions.
  • Protocols for V2X-based coordination must transmit intent information rather than relying on inference alone.
  • Accurate knowledge of other vehicles' plans becomes a prerequisite for reliable automated coordination.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Standards bodies could prioritize message formats that carry planned trajectories over relying on prediction modules.
  • The same intent-sharing advantage may appear in merging, intersection, or platoon maneuvers beyond the lane-change case studied.
  • Systems that combine modest prediction with intent sharing could reach coordination reliability with lower communication overhead than either method alone.

Load-bearing premise

A trajectory predictor that uses only current kinematic data serves as a representative baseline for what real-world V2X systems can accomplish without receiving explicit intent messages.

What would settle it

A follow-up simulation or field test that equips the ego vehicle with a more sophisticated kinematic-only predictor yet still records no gain in coordination success when intent messages are added.

Figures

Figures reproduced from arXiv: 2606.24203 by Javier Gozalvez, Miguel Sepulcre, Onur Altintas, Rafael Molina-Masegosa, Sergei S. Avedisov, Yashar Z. Farid.

Figure 1
Figure 1. Figure 1: Maneuver coordination with and without intent sharing. Red shading highlights elements introduced by intent-sharing. Naming conventions follow ETSI standards [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: ). For all considered vehicles, trajectories are computed using the same mobility models employed by the traffic simulation platform for modeling the vehicles’ mobility, including lane changes and accelerations/decelerations, except for the edge vehicles close to dpred for which a constant speed is assumed. The computed trajectories can have certain errors due to the limit imposed by dpred to achieve the n… view at source ↗
Figure 3
Figure 3. Figure 3: PDF (Probability Density Function) of the difference between the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: PDF (Probability Density Function) of the difference between the [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations require vehicles to accurately know other vehicles' driving intentions. Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle, as opposed to the ego vehicle predicting the trajectory on the remote vehicles' behalf. In this paper, we investigate the potential of intent-sharing on maneuver coordination effectiveness by quantifying the percentage of successful coordinations. We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario with the effectiveness of a trajectory prediction method based on current kinematic data. Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles' driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.

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 / 1 minor

Summary. The paper examines the role of intent-sharing in V2X maneuver coordination for CAVs. It compares direct communication of driving intentions against a trajectory-prediction baseline that relies solely on current kinematic data, claiming that intent-sharing yields substantial improvements in the percentage of successful coordinated lane changes in two highway scenarios.

Significance. If the quantitative results hold under a stronger baseline, the work would usefully highlight a concrete protocol-level requirement for V2X systems: that maneuver-coordination messages must carry explicit intent rather than relying on inference from kinematics alone. The simulation approach is a direct way to quantify coordination success, which is a strength for an applied networking paper.

major comments (2)
  1. [Abstract] Abstract and simulation-results section: the central claim of 'substantial improvements' is stated without any success-rate percentages, coordination-success definition, simulation parameters, or error metrics for the kinematic predictor. This prevents verification of the reported delta and directly affects the soundness of the headline result.
  2. [Simulation setup] Simulation setup (trajectory-prediction baseline): the comparator is described only as 'based on current kinematic data.' No detail is given on the extrapolation rule (constant velocity, constant acceleration, filtering, or history length), so it is impossible to judge whether the baseline is representative of production V2X predictors; any non-trivial predictor would shrink the measured gain.
minor comments (1)
  1. The two scenarios are mentioned but not labeled or described; adding a short table or figure caption that distinguishes them would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating revisions where the manuscript will be updated for greater clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract and simulation-results section: the central claim of 'substantial improvements' is stated without any success-rate percentages, coordination-success definition, simulation parameters, or error metrics for the kinematic predictor. This prevents verification of the reported delta and directly affects the soundness of the headline result.

    Authors: We agree that the abstract would benefit from explicit quantitative details to allow verification of the improvements. In the revised version, we will update the abstract to report the specific success-rate percentages for coordinated lane changes in the two scenarios, the definition used for a successful coordination, key simulation parameters (e.g., speeds, densities, communication range), and the error metrics applied to the kinematic predictor. Corresponding details will also be emphasized in the simulation-results section. revision: yes

  2. Referee: [Simulation setup] Simulation setup (trajectory-prediction baseline): the comparator is described only as 'based on current kinematic data.' No detail is given on the extrapolation rule (constant velocity, constant acceleration, filtering, or history length), so it is impossible to judge whether the baseline is representative of production V2X predictors; any non-trivial predictor would shrink the measured gain.

    Authors: The referee is correct that the baseline description lacks necessary specifics. Our implementation uses instantaneous constant-velocity extrapolation from the latest position and velocity values, with no filtering, acceleration model, or history window. We will expand the simulation-setup section to document this rule fully, including all parameters and assumptions. We will also note that while a more advanced predictor could narrow the gap, the chosen baseline serves to isolate the value of explicit intent over minimal kinematic inference; we will discuss this as a limitation and potential direction for future work. revision: yes

Circularity Check

0 steps flagged

No circularity; simulation comparison with no derivations or fitted predictions

full rationale

The paper is a comparative simulation study that quantifies successful maneuver coordination percentages under intent-sharing versus a kinematic-data baseline. No equations, parameter fits, uniqueness theorems, or self-citations are invoked to derive results; the central claim is an empirical delta from two simulation scenarios. This matches the default non-circular case for simulation work whose inputs (scenarios, coordination rules) are not redefined by the outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no modeling details, equations, or assumptions that can be audited.

pith-pipeline@v0.9.1-grok · 5702 in / 1056 out tokens · 20047 ms · 2026-06-25T22:14:19.613972+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

10 extracted references

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