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REVIEW 3 major objections 6 minor 26 references

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A single-camera vision-language model, fine-tuned with LoRA, achieved the top camera-only score of 0.8747 on the 2024 End-to-End Driving at Scale Challenge.

desk verdict A 2nd place leaderboard score with an honest limitations section, but the paper's central inference that the VLM causes the good performance is unsupported by any ablation. read the letter →

arxiv 2509.02659 v1 pith:L4T4324C submitted 2025-09-02 cs.CV cs.RO

Zilong Guo , Yi Luo , Long Sha , Dongxu Wang , Panqu Wang , Chenyang Xu , Yi Yang This is my paper
classification cs.CVcs.RO
keywords end-to-endautonomousdrivingvisionlanguagemodelsingle-cameraperceptiontrajectoryplanningLoRAfine-tuningnuPlanbenchmarkmultimodalLLM
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 paper tries to establish that a vision-language model—a large language model that also reads images—can serve as the core of an end-to-end autonomous driving system, and that this works even with a single front-facing camera. The authors build a pipeline that feeds camera images, ego-state history, a navigation command, and a text prompt into a 4-billion-parameter language model, and have the model output both a trajectory and a text explanation. On the 2024 End-to-End Driving at Scale Challenge, their entry scored 0.8747 and was the best camera-only result on the leaderboard. Because the authors report no comprehensive ablation studies, the paper demonstrates that such a system can perform well, but does not isolate which component causes the gain.

What carries the argument

The central object is the multimodal language model (InternLM 4B), which acts as a single reasoning module that ingests vision tokens, ego-state vectors, navigation tokens, and task prompts, and autoregressively emits both a trajectory and language. The trajectory is decoded from the last token by an MLP, producing location, velocity, and acceleration; the text follows. LoRA low-rank adaptation enables fine-tuning this LLM on the nuPlan driving data with an L2 trajectory loss and a next-token prediction text loss. The mechanism's work is to fuse all inputs into one autoregressive planning process, with the text output trained alongside the trajectory.

What would settle it

Train the identical pipeline but replace the LLM with an MLP trajectory head that consumes the same vision features and history vector; if the score reaches or exceeds 0.8747, the claim that the vision-language model is the source of the performance is refuted.

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Extended reading notes

Core claim

The central claim is that combining end-to-end architectural design with a knowledgeable vision-language model yields strong driving performance, and that this holds even with minimal sensing: only one camera and single-frame input. The model represents 'what we have seen' through a vision transformer, 'what we have done' through a small MLP on ego history, and 'where to go next' through navigation text, all fed into an InternLM 4B model fine-tuned with LoRA. The LLM outputs tokens that an MLP decodes into a trajectory vector of position, velocity, and acceleration, followed by text that explains the behavior. On the challenge's open-loop benchmark the method reached 0.8747, the top score am

Load-bearing premise

The paper's own stated limitation—no comprehensive ablation studies—means its central claim assumes the vision-language model, rather than the LoRA fine-tuning or the trajectory decoder, is responsible for the 0.8747 score.

Editorial extensions

If this is right

  • A single front-facing camera, without LiDAR or multiple views, can reach the top camera-only score in a large open-loop driving benchmark.
  • A general 4B vision-language model, adapted with LoRA, can learn to output plausible trajectories and driving text from modest amounts of driving language data.
  • One end-to-end network can cover diverse scenarios—straight driving, turns, traffic lights, stop signs, toll booths, and pedestrians—within a single autoregressive output.
  • The combination of trajectory and language output lets the model give a rationale for the plan, which the authors see as closer to human driving.

Reading between the lines

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

  • Because the paper has no ablation isolating the VLM, a fair test would replace the LLM with a non-autoregressive MLP trajectory head fed by the same vision and history features; if the score does not drop, the central claim about VLMs is unsupported.
  • The text output may act as an auxiliary training signal; a testable prediction is that removing the text loss while keeping the trajectory head would lower the trajectory score even at inference time.
  • The open-loop leaderboard number says nothing directly about closed-loop driving; the authors acknowledge this, and a closed-loop evaluation would be the natural next step.
  • The single-frame, single-camera design leaves temporal context unused; adding multi-frame input could plausibly improve both open- and closed-loop performance, but the paper does not show this.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper reports a second-place solution for the CVPR 2024 End-to-End Driving at Scale Challenge. The proposed architecture is an end-to-end vision-language model that takes a single camera image, ego-history features, a navigation command, and a text prompt; an InternLM-4B backbone generates both a trajectory (decoded by an MLP from the last token under L2 loss) and free-form text (under next-token prediction loss). The authors report a final score of 0.8747 and state that this is the best camera-only solution on the leaderboard. Qualitative visualizations are provided for scenarios such as intersections, traffic lights, stop signs, and toll booths. Section 3 (Limitations) explicitly acknowledges that no comprehensive ablation studies were conducted and that the open-loop setting may not reflect closed-loop performance.

Significance. If the reported leaderboard score is accurate, the result is a useful data point: a single-camera, VLM-based end-to-end architecture can rank highly on a standard open-loop driving benchmark. The external leaderboard provides a checkable empirical anchor, and the architecture description is clear. However, the manuscript does not include code, hyperparameters, ablations, or a comparison table, so the scientific contribution at present is mostly a plausible architecture plus a single benchmark score. The central causal claim that the vision-language model itself is responsible for the improvement is not isolated from other components, a limitation the authors openly acknowledge. The outcome is therefore significant if supported by additional experiments, but as it stands the evidence is too thin to support the advertised 'knowledgeable VLM' hypothesis.

major comments (3)
  1. [Abstract / §1 / §3] The central claim that combining the end-to-end design with a 'knowledgeable VLM' drives the strong result (Abstract, §1) is not supported by any ablation. The Limitations paragraph states 'we did not conduct comprehensive ablation studies.' Given the architecture in §2, the 0.8747 score could equally be explained by the pretrained vision encoder, the LoRA adapters, the trajectory MLP, or the L2 regression target. Without a non-LLM trajectory-head variant, a frozen-LLM variant, or a variant without the auxiliary text loss, the leaderboard score cannot isolate the VLM contribution. This is load-bearing and needs either experiments or a substantial weakening of the claim.
  2. [§2 Training / §3 Result] No experimental configuration is reported: number of epochs, LoRA rank/alpha, learning rate, batch size, prompt templates, loss weighting between trajectory L2 loss and text next-token loss, trajectory coordinate representation, ego-history vector composition, or inference-time settings. The leaderboard score is given without defining the evaluation metric or the number of runs. These omissions make the result unreproducible and prevent an independent assessment of whether 0.8747 is robust or a single-run occurrence. This is a major gap for a journal submission claiming a benchmark result.
  3. [§3 Result] The 'best camera-only solution' claim is not accompanied by a comparison table, a list of other camera-only submissions, or their scores. Because leaderboard positions can depend on team resources and post-processing, the rank alone is not interpretable in the manuscript. The qualitative generalization claims from Figure 2 (traffic lights, stop signs, toll booth, pedestrians) are not quantified, and no failure cases are shown. At minimum, a baseline table and some quantitative closed-loop or perceptual metrics should be provided, or the paper should be explicitly framed as a solution description rather than a scientific comparison.
minor comments (6)
  1. [§2 Decoder] Typo: 'LLm generates the output tokens' should be 'LLM generates the output tokens'.
  2. [§2 LLM] The model is named 'Intern-LM 4B' but reference [3] is InternLM2; please align the name and citation. Also specify the exact checkpoint and any chat-template usage.
  3. [§2 Training] The sentence 'The text output is also trained using driving language dataset such as [17]' is vague. Please specify the dataset, whether it is mixed with nuPlan data, the sampling ratio, and how the text output is used during inference.
  4. [Figure 1] The figure is not described in sufficient detail in the text. In particular, the exact format of the navigation command and the text prompt (with example strings) would help reproducibility.
  5. [References] Reference [1] is incomplete: the title is truncated and no URL is given. The leaderboard URL in footnote 1 should be formatted as a proper citation and checked for accessibility.
  6. [§3 Limitations] Minor wording: 'close-loop' should be 'closed-loop'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: external leaderboard result; admitted absence of ablations is a support gap, not a circular derivation.

full rationale

This paper is an empirical competition report rather than a derivation. It contains no equations, no fitted parameter that is later relabeled as a prediction, and no load-bearing appeal to the authors' own prior work. The reported score of 0.8747 and the 'best camera-only' rank are external benchmark outputs produced by the challenge organizers, not quantities that the paper constructs from its own assumptions. The architecture description (Section 2) combines standard components (ViT encoder, MLP ego-history encoder, InternLM-4B, LoRA, L2 trajectory loss, next-token text loss) but does not derive the leaderboard score from those choices in a way that would make the score equivalent to the input. The central causal claim — that the VLM, rather than the vision encoder, LoRA adapters, or trajectory head, is responsible for the score — is explicitly left unsupported in Section 3 (Limitations): 'we did not conduct comprehensive ablation studies.' That is a confound or evidence gap, not circularity: the conclusion is not built into the premise by definition, and no self-citation or constructed quantity forces the result. The cited foundation models (InternLM, nuPlan, DriveLM) are external works, and no uniqueness theorem or ansatz is imported from the authors' own prior publications. Therefore, under the circularity definitions, the paper scores 0: it may overclaim attribution to the VLM, but it does not reduce its prediction to its inputs in a circular manner.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new theoretical objects or fitted constants. Its assumptions are standard domain assumptions about using a pretrained LLM, the nuPlan benchmark, and L2 trajectory loss. The central weakness is that these assumptions are not tested through ablations.

assumptions (3)
  • domain assumption InternLM2 4B is a suitable backbone for trajectory generation
    Section 2 (LLM) states the authors use Intern-LM 4B and say any open-source model works; the choice of this specific model is not justified by experiments.
  • domain assumption The nuPlan benchmark score is a valid measure of driving competence
    Section 3 uses the CVPR 2024 Challenge leaderboard score as the sole quantitative evidence, and the Limitations note that the open-loop setting may not reflect real-world performance.
  • domain assumption L2 loss on trajectory coordinates is an appropriate training objective
    Section 2 (Training) states the trajectory output is trained with L2 loss without discussion of alternatives or ablations.

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Cite this review

Pith. "Pith review of 2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model." pith.science (2026). https://pith.science/paper/L4T4324C

@misc{pith2026250902659,
  author       = {Pith},
  title        = {Pith review of: 2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L4T4324C}},
  note         = {Machine review of arXiv:2509.02659}
}
read the original abstract

End-to-end autonomous driving has drawn tremendous attention recently. Many works focus on using modular deep neural networks to construct the end-to-end archi-tecture. However, whether using powerful large language models (LLM), especially multi-modality Vision Language Models (VLM) could benefit the end-to-end driving tasks remain a question. In our work, we demonstrate that combining end-to-end architectural design and knowledgeable VLMs yield impressive performance on the driving tasks. It is worth noting that our method only uses a single camera and is the best camera-only solution across the leaderboard, demonstrating the effectiveness of vision-based driving approach and the potential for end-to-end driving tasks.

Figures

Figures reproduced from arXiv: 2509.02659 by the authors.

Figure 1
Figure 1. The architecture of our network. Our network receives input from camera, ego history, navigation signal, and text prompt. Through various encoder and the LLM module, our network generates trajectories and texts simultaneously [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Visualization of the results of our method. References [1] Vicuna: An open-source chatbot impressing gpt-4 with 90 https://vicuna.lmsys.org/, 2023. 2 [2] Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, and Sammy Omari. nuplan: A closed-loop ml-based plan￾ning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810, 2021. 2 [3] Zheng Cai, Maos… view at source ↗

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Reviewed August 5, 2026 · model on record in the stance chip above.