Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles

Alexandru Kampmann; Bassam Alrifaee; David Kl\"uner; Hazem Ibrahim; Jianye Xu; Julius Kahle; Lucas Hegerath; Simon Sch\"afer

REVIEW 3 major objections 5 minor 270 references

Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Moving automated-vehicle software from simulation to full road tests is a ladder of eight requirements, and most testbed choices track convenience.

desk verdict Useful survey of CAV testbeds and middlewares, but the counts in Section 4.1 don't match Table 2, so the inductive findings need a documented sample and correction before they can be trusted. read the letter →

arxiv 2505.03472 v1 pith:R2LKCJ4E submitted 2025-05-06 cs.MA

David Klüner , Simon Schäfer , Lucas Hegerath , Jianye Xu , Julius Kahle , Hazem Ibrahim , Alexandru Kampmann , Bassam Alrifaee This is my paper · ORCID

classification cs.MA

keywords connectedandautomatedvehiclessoftwarearchitectureautomotivemiddlewaresimulationsmall-scaletestbedsfull-scalereal-timesystemstestbedselection

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This survey argues that connected and automated vehicle (CAV) software is validated in three stages—pure simulation, small-scale testbeds with miniature vehicles, and full-scale testbeds with real cars—and that each transition imposes a new set of software requirements. It catalogs 30 simulators, 54 small-scale testbeds, and 13 full-scale testbeds, and explains the middleware choices that appear at each scale. The paper's central conclusion is that the choice of testbed scale and software architecture is largely driven by simplicity and convenience rather than by a rigorous method. On that basis it issues four concrete recommendations, including testing planning and control at small scale and testing traffic management in simulation. A reader comes away with a checklist of eight requirements any CAV software framework must satisfy before moving toward road-ready vehicles.

What carries the argument

The load-bearing structure is a requirements ladder: a set of eight software requirements ordered by testbed scale, starting with energy efficiency and real-time execution at the small scale and ending with orchestration, safety and security, platform compatibility, and experiment recording at full scale. The ladder is built by comparing middleware use across 30 simulators, 54 small-scale testbeds, and 13 full-scale testbeds, grouped into seven simulator classes, nine small-scale categories, and three full-scale architecture types. It is the mechanism that connects observed testbed practice to the paper's recommendations.

What would settle it

A reproducible re-survey with explicit inclusion criteria that adds industrial and non-European testbeds would settle the inventory's representativeness; if it shows testbed and middleware choices tracking funding or regulation rather than simplicity, the paper's main conclusion is wrong. A second check is to look for a functioning full-scale CAV testbed whose software stack intentionally lacks one of the eight stated requirements and still passes safety review.

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Extended reading notes

Core claim

The paper's core claim is that there is a systematic ladder from simulation to small-scale to full-scale testing, and the ladder is defined by progressively stricter software requirements. In simulation, execution can be paused or accelerated, failures are cheap, and computing resources appear abundant; small-scale physical platforms add the need for real-time execution, deterministic and repeatable behavior, energy awareness, and support for multiple distributed agents linked by wireless communication; full-scale vehicles add orchestration and resource management, safety and security, compatibility with heterogeneous automotive hardware and protocols, and complete experiment recording. The paper derives eight requirements in total and uses them to explain four findings: real-time behavior should be verified with dedicated real-time hardware and hardware-in-the-loop; planning, control, racing, and platooning are best evaluated on small-scale testbeds; traffic management should be tested in simulation; and edge computing and V2X communication are best tested at small or full scale. Underlying all of this is the observation that the choice of both architecture and testbed scale is largely driven by simplicity and convenience.

Load-bearing premise

The survey assumes that its inventory of 30 simulators, 54 small-scale testbeds, and 13 full-scale testbeds is representative, even though it reports no search protocol, inclusion criteria, or time window, and the sample skews toward the authors' own testbeds and community.

Editorial extensions

If this is right

Researchers evaluating planning, control, racing, or platooning algorithms should choose a small-scale testbed, moving to simulation only for traffic-level questions.
Claims about real-time behavior should be demonstrated on dedicated real-time hardware or with hardware-in-the-loop testing before small- or full-scale validation.
Traffic management and city-scale flow questions belong in simulation rather than on physical testbeds.
V2X and edge-computing experiments should begin with network simulators and small-scale proofs of concept before moving to full-scale on-road systems.
New CAV middleware frameworks should be designed against all eight requirements from the start, since later stages of testing will demand them.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If testbed choice is indeed convenience-driven, a scoring tool that weighs the eight requirements against available testbed capabilities could replace ad hoc selection and is a direct next step the paper leaves implicit.
The eight requirements double as a maturity rubric: a software stack that cannot satisfy the full-scale requirements should not be presented as deployment-ready.
The survey's own inventory suggests that mixed-fidelity validation—simulation for traffic, small-scale for planning, full-scale for V2X—is the practical default, even though no single platform covers all needs.
A testable prediction follows from the convenience claim: testbeds whose platforms are harder to set up, for example those requiring custom middleware or strict real-time hardware, should be underused relative to their scientific value; a bibliometric analysis of which testbeds appear in method evaluations could check this.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: this is a genuinely useful survey but the numbers don't hold up. The paper claims 45 ROS-based small-scale testbeds, 4 custom, and 20 without middleware, but Table 2 — the paper's own table — shows 41 ROS, 3 custom, and 10 without. That is not a minor typo, because the findings in Section 8.2 are inductions from this table.

What's good: the survey is well-organized and covers a lot of ground. Thirty simulators, 54 small-scale testbeds, 13 full-scale testbeds, with middleware, focus, and publication data. The eight requirements (real-time, determinism, distributed systems, etc.) are a reasonable checklist, and the discussion of how AUTOSAR Adaptive, ROS 2, and DDS handle them is genuinely informative. Adding E/E and component simulator categories to Li et al.'s taxonomy is a small but sensible extension.

The soft spots are real. The selection method is never described: no search window, databases, or inclusion criteria. The tables are just 'what we found during our literature review.' That alone makes the prevalence claims fragile. On top of that, the count mismatch shows the table wasn't carefully checked. And the paper leans heavily on the authors' own CPM Lab and prior survey [166] in both the tables and the recommendations. Self-citation isn't inherently bad, but when the sample is undocumented, the overlap becomes a legitimate concern.

I should say the central argument — that requirements get stricter as you move from simulation to physical testbeds — holds up. It's a sensible qualitative claim, and the requirement sections support it. The problem is the paper then goes further and makes quantitative claims about what the field does ('ROS dominates', 'most testbeds are X') without a defensible sample.

So: not a new research result, and the inductive base needs work. But for someone entering CAV testing and wanting a map of simulators and testbeds, this would be a helpful starting point after revision. I'd send it to review with a demand for a proper methodology section and corrected counts. Right now I would not rely on its prevalence statistics, but the taxonomy and requirement analysis have value.

Referee Report

3 major / 5 minor

Summary. This survey reviews software frameworks, middlewares, simulators, and testbeds for connected and automated vehicles (CAVs). It proposes a seven-way simulator taxonomy, catalogs 30 simulators, 54 small-scale testbeds, and 13 full-scale testbeds, and derives eight requirements for software under test when moving from simulation to small-scale to full-scale. The paper concludes that the choice of architecture and testbed scale is largely driven by simplicity and convenience, and it makes four recommendations, e.g., planning and control should be tested on small-scale testbeds while traffic management should be evaluated in simulation.

Significance. If the inventory were representative, the paper would provide a useful map for selecting testbed scales and middlewares, and the requirements checklist could inform the design of new CAV software frameworks. The survey's strengths are its breadth and organization, and the distinction between API-based and middleware-based simulation architectures is helpful. However, the central findings are inductions from an inventory whose construction is not documented and whose internal counts are inconsistent; these issues must be resolved before the conclusions can be accepted as general.

major comments (3)

[Section 4.1 and Table 2] The text reports that 45 of the considered small-scale testbeds use ROS, 4 use custom middleware, and 20 do not use middleware, but Table 2 contains 54 testbeds and its middleware column lists 40 ROS-based, 4 custom, and 10 without middleware. The numbers are inconsistent in both readings (45+4+20=69 vs. 54; 40+4+10=54), and because the 'ROS dominates' claim and Finding 2 rely on this distribution, the counts must be reconciled and the affected claims re-derived.
[Sections 3-5] No search protocol, inclusion/exclusion criteria, database list, or time window is given for the inventory of simulators and testbeds. Since Findings 2-4 and the Section 9 conclusion generalize from this sample, the inductive basis is not verifiable; please add a methodology subsection and discuss potential selection bias, including the over-representation of the authors' own CPM Lab and related work in Table 2 and Section 8.2.
[Section 8.2 and Section 9] The recommendations, e.g., 'planning and control are best tested on a small-scale testbed,' are asserted as conclusions of the survey, but the link from the tabulated data to these preferences is never made explicit. In particular, Table 2 contains many small-scale testbeds for planning and control, yet the survey does not compare success metrics or costs against full-scale alternatives; please state which evidence in Tables 1-3 supports each finding, or present the findings as informed opinions rather than data-driven conclusions.

minor comments (5)

[Section 3.2 and Fig. 3] The caption of Fig. 3 states 'We found four classifications,' while the text and Table 1 present seven categories; the caption should be corrected.
[Section 8] The introductory paragraph says 'we summarize these recommendations in five findings in Section 8.2,' but Section 8.2 lists four findings; please align the counts.
[Section 5.1 and Table 3] The table caption reads 'Overview of Overview of the full-scale testbeds'; remove the duplicated phrase.
[Section 4.1 and Fig. 4] The text says testbeds are grouped into 9 categories, Fig. 4's caption lists 6 or 7 categories, and Table 2 shows 7 labeled groups plus education/communication entries; the category count should be made consistent across text, figure, and table.
[Table 1] The Year column should state whether it records first release, cited version, or last update; for example, LGSVL is listed as 2020 despite earlier releases, and the 'None' middleware entries could be clarified as 'not specified' rather than 'no middleware'.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the survey's conclusions are inductive generalizations from an external literature inventory, not derivations from its own inputs; self-citations are present but not load-bearing.

full rationale

This is a survey and inventory, not a derivation with predictive equations. The central outputs — the eight transition requirements (Sections 6-7) and the four findings (Section 8.2) — are presented as generalizations from the tabulated simulators and testbeds and from the cited literature; no output is obtained by substituting a fitted parameter back into the model that produced it, and no uniqueness theorem or adopted ansatz is imported from the authors' prior work. The paper does cite its own prior survey [166] for the claim that smaller-scale testing is preferred due to cost, multi-vehicle feasibility, and minimal failure consequences, and it repeatedly uses the authors' CPM Lab as an example (Table 2, Section 8.1), but these citations are corroborating context rather than the sole evidence for a conclusion, and the same points are supported by external sources such as [217] and [254]. The recommendations to use small-scale testbeds for planning and control and simulation for traffic management are inductive readings of the collected testbed inventory, not definitions in disguise. There are real validity concerns that are not circularity: the inventory selection process is undocumented (Sections 3-5 state only that simulators and testbeds were 'found during our literature review'), and Section 4.1's middleware counts (45 ROS, 4 custom, 20 without middleware) are inconsistent with Table 2's middleware column (roughly 40 ROS, 3-4 custom, 10 without middleware), which weakens the inductive basis of the findings. Because the paper contains no equation-level or definition-level reduction of a claimed result to its own input, the circularity score is minimal.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters or invented entities; the survey rests on domain assumptions and the authors' classification choices, which are not formally validated.

assumptions (4)

domain assumption Software architecture and middleware are critical determinants of safe and efficient CAV operation.
Stated in Section 1 citing [54]; this premise frames why the survey focuses on software frameworks rather than hardware or regulation.
domain assumption Three testing paradigms (simulation, small-scale testbeds, full-scale testbeds) constitute the standard evaluation routes for CAVs.
Section 2.5 introduces these three as 'commonly used'; the survey's structure follows from this assumption.
domain assumption A software framework can and should enable seamless transitions between testing stages.
Abstract and Section 1 list this as a requirement for frameworks; the paper does not empirically validate whether seamlessness is achievable.
ad hoc to paper The proposed seven-way simulator taxonomy, including the two new categories (E/E and component simulators), is a valid and useful classification.
Section 3.2 extends Li et al.'s five classes with two categories based on 'our findings'; no inter-rater validation or external benchmark is provided, so the taxonomy is a postulate of this paper.

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Cite this review

Pith. "Pith review of Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles." pith.science (2026). https://pith.science/paper/R2LKCJ4E

@misc{pith2026250503472,
  author       = {Pith},
  title        = {Pith review of: Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R2LKCJ4E}},
  note         = {Machine review of arXiv:2505.03472}
}

read the original abstract

Ensuring the safe and efficient operation of CAVs relies heavily on the software framework used. A software framework needs to ensure real-time properties, reliable communication, and efficient resource utilization. Furthermore, a software framework needs to enable seamless transition between testing stages, from simulation to small-scale to full-scale experiments. In this paper, we survey prominent software frameworks used for in-vehicle and inter-vehicle communication in CAVs. We analyze these frameworks regarding opportunities and challenges, such as their real-time properties and transitioning capabilities. Additionally, we delve into the tooling requirements necessary for addressing the associated challenges. We illustrate the practical implications of these challenges through case studies focusing on critical areas such as perception, motion planning, and control. Furthermore, we identify research gaps in the field, highlighting areas where further investigation is needed to advance the development and deployment of safe and efficient CAV systems.

Figures

Figures reproduced from arXiv: 2505.03472 by the authors.

**Figure 2.** The role of a middleware in the development of distributed software. The middleware functions as an intermediary [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Illustration of the classifications for simulations. We found four classifications: comprehensive simulator, traffic flow [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Illustration of the classifications for small-scale testbeds. We found six classifications: platooning, V2V communication, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Illustration of the classifications for full-scale testbeds. We found five classifications: standalone, V2V communication, [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Illustration depicting the requirements moving from simulation to small and from small to full scale. Requirements [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]

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