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arxiv: 2511.06874 · v2 · pith:ACWN477Rnew · submitted 2025-11-10 · 📡 eess.SP · cs.SY· eess.SY

Radio-Coverage-Aware Path Planning for Cooperative Autonomous Vehicles

Giuseppe Baruffa , Luca Rugini , Francesco Binucci , Fabrizio Frescura , Paolo Banelli , Renzo Perfetti This is my paper

Pith reviewed 2026-05-18 00:07 UTC · model grok-4.3

classification 📡 eess.SP cs.SYeess.SY
keywords autonomous vehiclespath planningradio coverageDijkstra algorithmA star algorithmwireless connectivitymapping errorcooperative systems
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The pith

Autonomous vehicle fleets can plan routes that favor strong radio coverage while keeping extra distance minimal.

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

The paper shows how to adjust standard path planning methods like Dijkstra and A* so that autonomous vehicles consider wireless signal quality alongside travel distance. This helps fleets maintain better connections for sending sensor data to remote servers in smart city setups. The vehicles map the area using their sensors as they go, achieving low error rates in coverage maps. By testing different ways to weight radio factors in the cost, the methods improve coverage without large detours.

Core claim

By introducing radio-related cost-weighting functions into common path planning algorithms, the best paths account for radio coverage experience in addition to traveled distance. Simulations show the mapping algorithm reaches error probability below 2 percent, and the planning extends radio coverage with only limited increase in distance compared to standard shortest-path methods.

What carries the argument

Radio-related cost-weighting functions applied to Dijkstra and A* algorithms, which balance distance and coverage metrics based on real-time sensor mapping of wireless areas.

If this is right

  • AVs maintain higher bit rates and lower latency for offloading tasks.
  • Reduced handover rates during movement across coverage areas.
  • Improved cooperation in unknown or changing environments.
  • Only small extra travel distance compared to conventional algorithms.

Where Pith is reading between the lines

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

  • This approach might reduce overall energy use by minimizing data retransmissions due to poor signals.
  • Integration with other traffic or obstacle data could further optimize fleet operations.
  • Real-world deployment would need to account for varying network loads not tested in simulation.

Load-bearing premise

That the radio coverage can be accurately mapped in real time by the vehicles' sensors and that weighting it in path costs yields net benefits without unmodeled costs.

What would settle it

Running the proposed path planning in a scenario where coverage maps have high error rates or where the weighted paths result in coverage no better than shortest paths.

Figures

Figures reproduced from arXiv: 2511.06874 by Fabrizio Frescura, Francesco Binucci, Giuseppe Baruffa, Luca Rugini, Paolo Banelli, Renzo Perfetti.

Figure 1
Figure 1. Figure 1: Overview of the fog computing scenario. The AVs are equipped with embedded computing power, but in some cases it might be better to offload some of the tasks (such as object detection, SLAM, etc.) to external services [26] instantiated in computational nodes (CN) or data centers (DC) [27, 28] by exploiting the wireless link offered by access points (AP) or BSs [8]. Due to the strict communication latency r… view at source ↗
Figure 2
Figure 2. Figure 2: Graphical overview of the radio-aware path planning model. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Generation of the path CM from path WK, and of the map P from path CM. Cyan and green pixels represent the synthesized path CM. The system model used in the radio-coverage aware path planning problem is shown in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Expansion of the frontier in the Dijkstra/A* algorithm. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Probability of error map for scenario in Figure 5. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Obstacle map coverage as a function of the number of AVs. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Exploration time and probability of obstacle map error versus [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 15
Figure 15. Figure 15: Paths obtained with OD, WD (a), and WA (b) for different [PITH_FULL_IMAGE:figures/full_fig_p009_15.png] view at source ↗
Figure 12
Figure 12. Figure 12: Paths obtained with OD, WD (a), WA (b), and amplitude [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Paths obtained with OD, WD (a), WA (b), and capacity weight. [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Paths obtained with OD, WD (a), WA (b), and tent weight. [PITH_FULL_IMAGE:figures/full_fig_p009_14.png] view at source ↗
Figure 19
Figure 19. Figure 19: Run time increase as function of [PITH_FULL_IMAGE:figures/full_fig_p010_19.png] view at source ↗
Figure 18
Figure 18. Figure 18: Combined metric decrease as function of α. performance, in terms of radio metric. For both WD and WA, the on-off and tent weights have a similar behavior: WD achieves a radio metric gain of 20%, initially, growing up to about 70% [PITH_FULL_IMAGE:figures/full_fig_p010_18.png] view at source ↗
read the original abstract

Fleets of autonomous vehicles (AV) often are at the core of intelligent transportation scenarios for smart cities, and may require a wireless Internet connection to offload computer vision tasks to data centers located either in the edge or the cloud section of the network. Cooperation among AVs is successful when the environment is unknown, or changes dynamically, so as to improve coverage and trip time, and minimize the traveled distance. The AVs, while mapping the environment with range-based sensors, move across the wireless coverage areas, with consequences on the experienced access bit rate, latency, and handover rate. In this paper, we propose to modify the cost of common path planning algorithms such as Dijkstra and A*, so that the best path solution takes into account not only the traveled distance, but also the radio coverage experience. To this aim, several radio-related cost-weighting functions are introduced and tested, to assess the performance of the proposed approaches with extensive simulations. The proposed mapping algorithm can achieve a mapping error probability below $2\%$, while the proposed path-planning algorithms extend the radio coverage of the AVs, with only a limited increase in traveled distance with respect to shortest-path existing methods, such as conventional Dijkstra and A* algorithms.

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

3 major / 2 minor

Summary. The manuscript proposes modifying standard path-planning algorithms (Dijkstra and A*) for cooperative autonomous vehicles by incorporating radio-related cost-weighting functions. This allows paths to account for both traveled distance and radio coverage experience while the vehicles simultaneously map the environment using range-based sensors. Through simulations, the authors claim their mapping algorithm achieves a mapping error probability below 2% and that the radio-aware planners extend coverage with only a limited increase in distance relative to conventional shortest-path baselines.

Significance. If the simulation results prove robust and transferable, the work could support improved wireless connectivity and reduced handovers for AV fleets in dynamic smart-city scenarios. The explicit comparison against Dijkstra and A* baselines and the testing of multiple weighting functions are constructive elements that allow trade-off evaluation. However, the overall significance remains constrained by the absence of any validation against measured RF data or real sensor noise models.

major comments (3)
  1. [Abstract] Abstract: The central performance claims (mapping error probability below 2% and radio-coverage extension) are stated without any description of the simulation environment, propagation model, sensor noise characteristics, or the procedure used to construct radio coverage maps from range-based sensor outputs. This omission prevents verification that the reported gains are not simulation artifacts.
  2. [Path Planning Algorithms] Path-planning formulation: The radio cost-weighting functions presuppose that accurate radio coverage information is available in real time. Range-based sensors (LiDAR/camera) produce geometric occupancy grids, not RF signal strength or coverage boundaries; the manuscript does not specify how an external propagation model is applied or validated against RSSI/ray-tracing ground truth.
  3. [Simulation Results] Simulation results: No error bars, data-exclusion criteria, or sensitivity analysis on the free radio-cost parameters are provided. Consequently, it is impossible to determine whether the limited distance penalty and coverage gains hold under realistic sensor noise or varying propagation conditions.
minor comments (2)
  1. [Notation] Notation for the radio cost functions should be introduced with a clear table or equation list to improve readability.
  2. [Figures] Figure captions should explicitly state the radio weighting parameters used in each plotted curve.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We are grateful to the referee for the insightful comments, which have helped us identify areas for improvement in the manuscript. We respond to each major comment in turn.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance claims (mapping error probability below 2% and radio-coverage extension) are stated without any description of the simulation environment, propagation model, sensor noise characteristics, or the procedure used to construct radio coverage maps from range-based sensor outputs. This omission prevents verification that the reported gains are not simulation artifacts.

    Authors: We agree that the abstract would benefit from additional context on the simulation setup to support the stated claims. In the revised manuscript we will expand the abstract to briefly reference the urban grid simulation environment, the log-distance path-loss propagation model, and the range-sensor mapping procedure used to build coverage estimates. Full details remain in Section III, but this addition will allow readers to better assess the results. revision: yes

  2. Referee: [Path Planning Algorithms] Path-planning formulation: The radio cost-weighting functions presuppose that accurate radio coverage information is available in real time. Range-based sensors (LiDAR/camera) produce geometric occupancy grids, not RF signal strength or coverage boundaries; the manuscript does not specify how an external propagation model is applied or validated against RSSI/ray-tracing ground truth.

    Authors: The approach does assume radio coverage estimates are available during planning. In the revised Section II we will explicitly describe the two-layer mapping: range-based sensors produce occupancy grids for obstacles, while a parameterized log-distance propagation model (updated cooperatively) supplies the radio-coverage layer used by the weighted cost functions. This is a deliberate modeling choice to isolate the path-planning contribution; direct RSSI or ray-tracing validation lies outside the present simulation study. revision: partial

  3. Referee: [Simulation Results] Simulation results: No error bars, data-exclusion criteria, or sensitivity analysis on the free radio-cost parameters are provided. Consequently, it is impossible to determine whether the limited distance penalty and coverage gains hold under realistic sensor noise or varying propagation conditions.

    Authors: We acknowledge the lack of statistical detail in the current results section. The revised manuscript will report error bars (standard deviation over 100 Monte Carlo runs), state the data-exclusion rule (discarding trials with mapping failure), and add a dedicated sensitivity subsection that varies the radio-cost weighting coefficients and sensor-noise variance to confirm robustness of the distance-coverage trade-off. revision: yes

standing simulated objections not resolved
  • Absence of validation against measured RF data or real sensor noise models

Circularity Check

0 steps flagged

No circularity: simulation-based evaluation of modified path costs relies on external benchmarks

full rationale

The paper introduces radio-related cost-weighting functions to augment standard Dijkstra and A* algorithms and evaluates them via extensive simulations against conventional shortest-path baselines. No equations, derivations, or fitted parameters are shown that reduce by construction to the inputs; the mapping error probability and coverage-extension claims are assessed through independent simulation runs rather than self-referential definitions or self-citation chains. The approach is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so the ledger cannot be populated with concrete entries from the manuscript. The approach implicitly assumes standard graph-based path planning and the existence of measurable radio coverage maps that can be turned into additive costs.

free parameters (1)
  • radio cost weighting parameters
    Several radio-related cost-weighting functions are introduced; these functions almost certainly contain tunable or fitted scalars that control the trade-off between distance and coverage.

pith-pipeline@v0.9.0 · 5534 in / 1147 out tokens · 33744 ms · 2026-05-18T00:07:56.525580+00:00 · methodology

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Reference graph

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