PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning

Andreas Geiger; Andreas Look; Faris Janjo\v{s}; J\"urgen Mathes; Volodymyr Havrylov

REVIEW 5 major objections 5 minor 36 references

PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that vision-language planning supervision helps end-to-end driving models because it ties latent features to ground-truth future trajectories, not because the text encoder understands driving; it then proposes an ELBO-base

desk verdict Useful ablation showing CLIP isn't needed for VLP, but the headline collision number is contradicted by their own Table I and the 'implicit ELBO' claim is asserted without derivation. read the letter →

arxiv 2608.01201 v1 pith:L2OIRV2K submitted 2026-08-02 cs.RO cs.CV

Volodymyr Havrylov , Faris Janjoš , Andreas Look , Jürgen Mathes , Andreas Geiger This is my paper

classification cs.ROcs.CV

keywords end-to-endautonomousdrivingdeepsupervisionevidencelowerboundvariationalinferencelatent-spaceregularizationvision-languageplanningnuScenesmotion

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

The paper tries to establish that the empirical gains of vision-language planning supervision for end-to-end autonomous driving come from aligning internal latents with ground-truth future trajectories, not from the reasoning power of a frozen text encoder. It then proposes PRISM, a probabilistic deep-supervision framework that models those latents as Gaussian distributions and trains with an evidence-lower-bound objective instead of a heuristic contrastive loss. On nuScenes with a VAD-Tiny backbone and a shortened 35-epoch schedule, the framework reports about 8% lower planning L2 error and 3% lower collision rate than strong vectorized baselines, with zero inference overhead. If correct, this gives a cheaper and theoretically grounded recipe for latent supervision: any ground-truth signal, not language, can regularize intermediate driving representations.

What carries the argument

The engine of the method is the conditional ELBO objective $$J_{\text{CVAE}} = \mathbb{E}_{z \sim q_\$\varphi$(z|x,y)}[\log p_\$\theta$(y|x,z)] - \$\beta$ D_{\text{KL}}(q_\$\varphi$(z|x,y) \| p_\psi(z|x))$$ applied to the ego-query latent $z$ of the VAD planner. $p_\psi(z|x)$ is a Gaussian prior read off the driving input $x$, $q_\phi(z|x,y)$ is a Gaussian posterior read off the GT future trajectory and navigation command (discarded at inference), and $p_\theta(y|x,z)$ is the planning decoder. The KL term is what ties model latents to GT trajectories, and sampling with $S=2$ posterior samples stabilizes the reconstruction term. The supporting conceptual claim is that VLP's contrastive loss is an implicit vers

What would settle it

Derive the VLP objective (Eq. 6) and check whether it equals Eq. (7) with $q_\phi$ as a Dirac distribution; if the losses are not equal in the relevant limit, the claimed grounding fails. Experimentally, train the same pipeline with a frozen text encoder whose output features are replaced by random noise matched in mean and covariance: if planning quality drops, then the text-semantic stream carries information the paper's mechanism does not credit.

Watch

Extended reading notes

Core claim

The central discovery is that what made VLP work was not the semantic content of the CLIP text encoder but the forced correspondence between the model's latent ego queries and GT-annotated future trajectories. The paper supports this with ablations: SLP-only query-level supervision matches ALP+SLP, a simple trainable MLP GT encoder matches or beats the frozen CLIP encoder, and future-trajectory injection is the signal that matters. The paper then formalizes this as an implicit ELBO optimization in which VLP's deterministic contrastive alignment acts like a Dirac (point-mass) posterior, and replaces it with a Gaussian posterior over the same latents, trained by the conditional ELBO with the r

Load-bearing premise

The load-bearing premise is that VLP's contrastive alignment loss is mathematically an ELBO with a point-mass (Dirac) posterior; the paper states this equivalence as analysis but never derives it from Eq. (6), so if the equivalence fails, the theoretical justification for PRISM's ELBO design weakens even though the empirical planner improvements could survive.

Editorial extensions

If this is right

If the mechanism is right, any deep-supervision signal that ties the ego-query latent to future GT trajectories should improve planning, regardless of whether it is encoded as text, MLP features, or another modality.
Frozen text encoders are unnecessary for latent supervision; the paper's MLP encoder matches or beats CLIP, so language-driven supervision can be replaced by direct GT encoders with less overhead.
The ELBO objective with two posterior samples yields roughly 8% lower planning L2 error and 3% lower collision rate than the deterministic VLP baseline on nuScenes.
The learned latent distribution is uni-modal in practice, so within this architecture the ELBO acts as a regularizer, not a multi-modal generative model; richer diversity requires a generative backbone.

Reading between the lines

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

Editorial inference: the same simplification likely applies to later VLM-supervision methods; if their gains come from latent-GT alignment rather than language semantics, their frozen text encoders could be replaced by cheap GT encoders with little performance loss.
Editorial inference: the Dirac-equivalence account implies a general design rule for latent deep supervision: the target distribution should be built from the prediction target (future GT) rather than auxiliary semantic labels, since past-trajectory injection gave negligible signal.
Editorial inference: a testable extension is to apply the same ELBO objective to agent and map queries with attention-based or flow-based posterior encoders; the paper's mode-collapse analysis suggests such encoders could restore diversity while keeping the planning gains.

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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

The genuinely useful piece is the ablation: replacing the frozen CLIP text encoder with a small MLP that consumes GT attributes works as well or better, and SLP alone does most of the work. That tells the community something real about VLP and is worth preserving.

The soft spots are load-bearing. The abstract's 3% collision reduction is not in Table I: the S=2 configuration the authors champion has a 3s collision rate of 0.66 versus 0.46 for deterministic SLP. Averaged over horizons it's a wash or worse. The paper itself admits collision rates are high-variance and says L2 is the primary metric, but then the headline claim picks a number that their own table contradicts. That's a central-claim problem.

The second issue is the claimed equivalence between VLP's contrastive alignment and an ELBO with a Dirac posterior. They state it as analysis but never derive it from their Eq. 6. Without a derivation, the paper's theoretical framing is just an analogy, and it doesn't explain why the Gaussian posterior would be better than a point estimate in a way that is separately tested.

Third, the L2 gain appears only with S=2 multi-sample estimation. S=1 is worse than the deterministic baseline at every horizon, so the "probabilistic" advantage is entangled with variance reduction from averaging two samples. They don't control for that by, say, averaging two deterministic forward passes or using a different estimator. So the empirical claim collapses to: an MLP-encoded, SLP-only deep supervision scheme, combined with two-sample reconstruction, improves L2 over their VLP reimplementation. That is a much more modest claim than the abstract makes.

To be fair, the paper is transparent about baselines, reports learning curves and standard deviations, and flags the mode collapse and the collision variance. That's good practice. But the combination of an internally contradicted headline number and an asserted-but-not-derived theory is enough to reject in my view. The MLP-vs-CLIP result deserves a short paper on its own.

Recommendation: don't desk-reject on topic; a serious referee could be sent the revised version if the authors drop the collision claim and either prove or delete the ELBO equivalence. As submitted, I would reject but invite a revision that narrows the claims.

Referee Report

5 major / 5 minor

Summary. The paper studies latent-space deep supervision for end-to-end autonomous driving. It analyzes VLP, which uses a frozen CLIP text encoder to align intermediate features with GT annotations, and argues that VLP's gains come from connecting model latents to GT future trajectories rather than from language-based reasoning. The authors then propose PRISM, a probabilistic deep-supervision framework that models the ego-query as a Gaussian distribution and trains with an ELBO objective (Eq. 7), replacing the text encoder with an MLP and using only the SLP branch. Experiments on nuScenes with VAD-Tiny report roughly 8% lower L2 error for the S=2 probabilistic configuration versus a deterministic SLP baseline, and the abstract claims a 3% collision-rate decrease. The paper also reports learning curves, variance across runs, and ablations of ALP vs SLP, text vs MLP encoders, and objective choices.

Significance. If the claims were fully supported, the contribution would be useful: it simplifies VLP-style latent supervision, removes the expensive text encoder, and provides a probabilistic reinterpretation of latent alignment. The paper is transparent in several respects—it reports multi-run statistics, learning curves, acknowledges the collision-rate gap in its VLP reimplementation, and releases code. However, the central advertised results are currently overstated. The collision claim is inconsistent with the paper's own Table I, the VLP-as-ELBO equivalence is asserted without derivation, and the S=2 L2 improvement is confounded with multi-sample estimation because S=1 is worse than the deterministic baseline. The generative mode-collapse admitted in Sec. IV.D further undermines the 'capture uncertainty' framing. A cleaned, honestly scoped version of this study would be a solid empirical contribution, but the manuscript in its current form needs substantial revision.

major comments (5)

[Abstract, §I (Contributions), Table I] The abstract and contributions claim 'a 3% decrease in collision rates' and 'consistently reduces planning L2 error and collision rate.' Table I does not support the collision claim. Comparing Probabilistic ELBO (S=2) with the Deterministic SLP baseline, the 1s/2s/3s collision rates change as 0.21→0.13, 0.29→0.26, and 0.46→0.66, i.e. a 43% relative increase at 3s. The average relative change is negative, and even under a percentage-point interpretation the average masks a 20-point degradation at 3s. Section IV.A itself disclaims collision rates as too high-variance for robust comparison. The '3% decrease' claim must be corrected or replaced with a statistically valid comparison, and the 'consistently reduces ... collision rate' wording should be qualified.
[§I, §III.C, §IV.C (Eqs. 6–7)] The paper's central theoretical claim—that VLP's contrastive alignment is 'functionally, albeit implicitly, an ELBO with a Dirac posterior'—is asserted in the Introduction and again in Sec. IV.C, but never derived. Eq. (6) defines L_VLP as ERM plus cross-entropy contrastive losses on cosine-similarity matrices, while Eq. (7) is the standard CVAE ELBO with a Gaussian posterior and KL divergence. No algebraic or information-theoretic argument maps one objective to the other. If the equivalence is retained, the authors should supply the derivation; otherwise the explanatory claim in the Abstract and Conclusion should be explicitly downgraded to a heuristic analogy.
[Table I, §IV.C] The empirical advantage of the probabilistic method is entirely dependent on S=2. At S=1, the ELBO is worse than the deterministic SLP baseline on L2 at all horizons (1s 0.37 vs 0.33; 2s 0.63 vs 0.57; 3s 0.95 vs 0.88). The paper attributes the S=2 gain to 'drawing multiple samples ... to tighten the ELBO bound and reduce estimator variance,' but this confounds the probabilistic Gaussian parameterization with multi-sample estimation. A control experiment is needed—for example, applying the same two-sample estimation to the deterministic SLP alignment, or sweeping S=3,4—to establish that the Gaussian ELBO, rather than variance reduction from averaging, drives the reported L2 improvement. The current comparison does not justify the claim that the probabilistic formulation itself is the source of the gains.
[§IV.D, §I (Contributions)] Section IV.D reports a collapse toward uni-modal distributions and states that 'the main practical benefit of the probabilistic formulation ... [is] stronger latent regularization and improved planning accuracy, rather than richer multi-modal generation.' This directly contradicts the contribution statement that latents are modeled as reparameterizable distributions 'to capture uncertainty,' and it undermines the CVAE motivation in §II.C. The paper should either provide evidence of meaningful uncertainty modeling or reframe the probabilistic contribution as a regularizer that happens to be implemented with Gaussian latents. As written, the probabilistic interpretation of the method is not substantiated by the generative behavior.
[§IV.A, Table I, Abstract] The abstract refers to 'competitive vectorized baselines,' but the experiments compare only VAD-Tiny and the authors' own re-implementations of VLP's SLP branch ('Deterministic SLP'). The manuscript acknowledges a collision-rate gap with the published VLP baseline and uses a shortened 35-epoch schedule. Given the reported standard deviations, no significance tests are provided, and the collision numbers overlap substantially. The headline claims should be tied explicitly to the in-house re-implementation and supported by significance tests or confidence intervals, rather than presented as a comparison against published strong baselines.

minor comments (5)

[General] The name 'PRISM' appears in the title and abstract but is not used at all in the main text; please introduce it at first use in Sec. I and use it consistently.
[Fig. 2] The figure contains a box labeled 'Detailed description of LLM-based decoder.' No LLM is used in the paper; this label appears to be a leftover artifact and should be removed or corrected.
[Table I] The table heading 'Probabilistic Framework (VAD-VLP, SLP-Only)' is confusing because the 'Deterministic SLP' row is also a VAD-VLP SLP-only configuration. Please clarify the naming to distinguish the deterministic baseline from the probabilistic variants.
[§IV (Implementation Details)] The choice of KL weight beta=0.1 and hidden dimension D=256 is reported, but no sensitivity analysis is provided for these hyperparameters. Given that the S=2 result is a single configuration, a brief ablation or discussion of sensitivity would strengthen the empirical claims.
[Title and Abstract] There are minor spacing/formatting issues, e.g., 'Pr oblem' in the title and 'V AD' with a stray space in several places. These should be cleaned up in the final version.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the ELBO derivation is standard and the reported planning gains are empirical, not forced by construction.

full rationale

The paper's core derivation (Eq. 7) is a standard conditional ELBO: J = E_{qφ(z|x,y)}[log pθ(y|x,z)] − β KL(qφ ∥ pψ). The posterior encoder qφ is trained with GT y during training and discarded at inference, so the claimed planning improvement is an empirical outcome, not a quantity forced by construction. The ablations (ALP vs SLP, TE vs MLP, future vs past trajectories) are independent experiments; the L2 numbers in Table I are external to the math. No load-bearing self-citation appears: [18] (VLP) and [9] (VAD) are third-party baselines, and the paper does not invoke a uniqueness theorem. The main caveats — the assertion that VLP 'implicitly' optimizes a Dirac-posterior ELBO is stated rather than derived, and S=2 was selected after observing S=1 results — concern evidentiary support and hyperparameter selection, not definitional circularity. The collision-rate claim is internally inconsistent with Table I (3s collision increases), but that is a correctness/reporting issue, not a circularity issue.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The framework relies on standard CVAE machinery. The main non-standard premise is the unproved equivalence between VLP and a Dirac-posterior ELBO, plus hand-chosen hyperparameters beta, S, and gradient isolation that are not sensitivity-analyzed.

free parameters (4)

KL weight beta = 0.1
Chosen by hand; controls regularization strength and affects stability and performance. No sensitivity analysis is provided.
Number of ELBO samples S = 2
Selected after S=1 underperformed; the reported gains depend on this post-hoc choice.
Gradient isolation on prior features = applied
Hand-designed training trick; the paper offers no analysis of its necessity or interaction with beta.
Hidden dimension D of MLP heads = 256
Architectural choice for the prior, posterior, and projection MLPs; not varied.

assumptions (6)

domain assumption VAD-Tiny can be treated as a conditional generative model p(y|x,z) with latent variables.
The paper reinterprets a discriminative planner as a generative model without discussing whether the architecture supports this.
domain assumption GT future trajectories are a valid privileged signal for latent regularization that transfers to inference.
Standard in deep supervision, but the paper does not analyze distribution shift between training-time GT injection and inference-time prior sampling.
ad hoc to paper VLP's contrastive loss is functionally an ELBO with a Dirac posterior.
Stated as 'our analysis reveals' in Section I and V, but no equation maps Eq. 6 to Eq. 7; this is the central unsupported premise.
domain assumption Latent distributions are Gaussian with diagonal covariance.
A modeling choice that is standard for CVAEs but not justified for driving latents in this paper.
standard math The reparameterization trick and the ELBO decomposition.
Unproved background from Kingma and Welling (2014) and amortized inference literature.
ad hoc to paper Regularizing only the ego-agent query is sufficient.
The paper drops ego-map query supervision and ALP based on ablations, but this choice is not theoretically grounded.

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Cite this review

Pith. "Pith review of PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning." pith.science (2026). https://pith.science/paper/L2OIRV2K

@misc{pith2026260801201,
  author       = {Pith},
  title        = {Pith review of: PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L2OIRV2K}},
  note         = {Machine review of arXiv:2608.01201}
}

read the original abstract

End-to-end autonomous driving (E2E AD) systems integrate perception, prediction, and planning into a single differentiable architecture. While these models show great promise, their standard training often relies on output-only supervision, which can lead to weak gradients for the hidden layers of increasingly complex models. Recent works have integrated vision-language model (VLM) supervision for latent features to address this, yielding substantial empirical gains, yet leaving the underlying theoretical mechanisms poorly understood. Our investigation into this methodology reveals that the resulting performance gains stem not from VLM reasoning capabilities, as previously assumed, but rather from the latent connections forged between the E2E AD model and ground-truth (GT) data during training. Building on this insight, we propose a probabilistic deep supervision framework that regularizes intermediate latent representations directly from GT data. By treating model latents as reparameterizable distributions, we optimize the architecture via the Evidence Lower Bound (ELBO). Our evaluations conducted on the nuScenes dataset demonstrate that supervising trajectory-related latents with future GT paths consistently improves planning performance. Using identical training data and E2E architectures, our method achieves an 8% reduction in planning L2 error and a 3% decrease in collision rates compared to competitive vectorized baselines, all while incurring negligible computational overhead.

Figures

Figures reproduced from arXiv: 2608.01201 by the authors.

**Figure 1.** High-level comparison of conventional supervised (left) and deep supervision (middle and right) training paradigms. Our proposed deep supervision approach PRISM captures the interplay between a context x and ground-truth future y into a probabilistic representation without requiring expensive text encoders. high-level waypoints [8, 9, 15]. In these setups, loss functions are applied solely at the output level. Altho… view at source ↗

**Figure 2.** Proposed probabilistic deep supervision framework. PRISM models target latent features as distributions and follows an ELBO-based training objective. GT encoder has an MLP-based architecture, omiting using text encoders. terminology throughout this work. Our analysis reveals that VLP’s methodology functionally, albeit implicitly, optimizes a simplified version of the Evidence Lower Bound (ELBO), using Dirac distrib… view at source ↗

**Figure 3.** VAD [9] architecture. The image backbone lifts multi-view inputs into BEV features. A vectorized scene-learning stage then encodes salient scene components (lanes, road boundaries, dynamic agents) as queries. These queries condition an ego-vehicle query via an interaction mechanism, which is subsequently optimized for the planning objective. theoretically-grounded and optimal ways to leverage GT for latent feature r… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Description of VLP [18] framework. During training, GT annotations are represented as structured prompts, encoded by a frozen text encoder and aligned with internal visual features via a contrastive objective. ALP supervises agent-centric BEV regions, and SLP supervise…

**Figure 5.** Figure 5: Learning curves comparing original deterministic VLP and our probabilistic extension over 35 epochs. Thick lines denote the mean over two independent runs, with shaded regions indicating ±1 standard deviation. We use only the SLP branchs. S denotes the number of sample…

**Figure 6.** Figure 6: Overview of GT data encoders that transform input data into target [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Reference graph

Works this paper leans on

36 extracted references · 29 canonical work pages

[1]

Critical reasons for crashes investigated in the national motor vehicle crash causation survey,

S. Singh, “Critical reasons for crashes investigated in the national motor vehicle crash causation survey,”DOT HS, 2015

work page 2015
[2]

Optimizing autonomous transfer hub networks: Quantifying the potential impact of self-driving trucks,

C. Lee, K. Dalmeijer, P. V . Hentenryck, and P. Zhang, “Optimizing autonomous transfer hub networks: Quantifying the potential impact of self-driving trucks,”EURO J. Transp. Logist., vol. 13, p. 100141, 2024

work page 2024
[3]

Challenges of autonomous driving trucks and the impact on logistics,

E. Gjere, “Challenges of autonomous driving trucks and the impact on logistics,” 2023, accessed: 2025-08-30. [Online]. Available: https://www.theseus.fi/handle/10024/806084

work page 2023
[4]

Autonomous high speed road vehicle guidance by computer vision,

E. Dickmanns and A. Zapp, “Autonomous high speed road vehicle guidance by computer vision,”IFAC Proceedings Volumes, vol. 20, no. 5, Part 4, pp. 221–226, 1987, 10th Triennial IFAC Congress on Automatic Control - 1987 V olume IV , Munich, Germany, 27-31 July

work page 1987
[5]

Autonomous driving in urban environments: Boss and the urban challenge,

C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer,et al., “Autonomous driving in urban environments: Boss and the urban challenge,”Journal of Field Robotics, vol. 25, no. 8, pp. 425–466, 2008

work page 2008
[6]

A survey of autonomous driving: Common practices and emerging technologies,

E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,” IEEE Access, vol. 8, pp. 58 443–58 469, 2020

2020
[7]

ALVINN: an autonomous land vehicle in a neural network,

D. Pomerleau, “ALVINN: an autonomous land vehicle in a neural network,” inAdvances in Neural Information Processing Systems 1, [NIPS Conference, Denver, Colorado, USA, 1988]. Morgan Kaufmann, 1988, pp. 305–313

work page 1988
[8]

Planning-oriented autonomous driving,

Y . Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, L. Lu, X. Jia, Q. Liu, J. Dai, Y . Qiao, and H. Li, “Planning-oriented autonomous driving,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023. IEEE, 2023, pp. 17 853–17 862

work page 2023

Show all 36 references

[9]

V AD: vectorized scene representation for efficient autonomous driving,

B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q. Zhang, W. Liu, C. Huang, and X. Wang, “V AD: vectorized scene representation for efficient autonomous driving,” inIEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023. IEEE, 20...

2023
[10]

Sparsedrive: End-to-end autonomous driving via sparse scene representation,

W. Sun, X. Lin, Y . Shi, C. Zhang, H. Wu, and S. Zheng, “Sparsedrive: End-to-end autonomous driving via sparse scene representation,” CoRR, vol. abs/2405.19620, 2024

2024 arXiv
[11]

The NVIDIA pilotnet experiments,

M. Bojarski, C. Chen, J. Daw, A. Degirmenci, J. Deri, B. Firner, B. Flepp, S. Gogri, J. Hong, L. D. Jackel, Z. Jia, B. J. Lee, B. Liu, F. Liu, U. Muller, S. Payne, N. K. N. Prasad, A. Provodin, J. Roach, T. Rvachov, N. Tadimeti, J. E. van Engelen, H. Wen, E. Yang, and Z. Yang,...

2010 arXiv
[12]

Learning accurate, comfortable and human-like driving,

S. Hecker, D. Dai, and L. V . Gool, “Learning accurate, comfortable and human-like driving,”CoRR, vol. abs/1903.10995, 2019

1903 arXiv
[13]

End-to-end learning of driving models from large-scale video datasets,

H. Xu, Y . Gao, F. Yu, and T. Darrell, “End-to-end learning of driving models from large-scale video datasets,” in2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. IEEE Computer Society, 2017, pp. 3530– 3538

2017
[14]

End to end learning for self-driving cars,

M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba, “End to end learning for self-driving cars,” CoRR, vol. abs/1604.07316, 2016

2016 arXiv
[15]

Multi-modal fusion transformer for end-to-end autonomous driving,

A. Prakash, K. Chitta, and A. Geiger, “Multi-modal fusion transformer for end-to-end autonomous driving,” inIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. Computer Vision Foundation / IEEE, 2021, pp. 7077–7087

2021
[16]

Deeply- supervised nets,

C. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu, “Deeply- supervised nets,” inProceedings of the Eighteenth International Con- ference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May 9-12, 2015, vol. 38. JMLR.org, 2015

2015
[17]

iMacHSR: intermediate multi-access heterogeneous supervision and regularization scheme toward architecture-agnostic training,

W.-B. Kou, G. Zhu, Y . Jin, S. Wang, M. Tang, and Y .-C. Wu, “iMacHSR: intermediate multi-access heterogeneous supervision and regularization scheme toward architecture-agnostic training,”CoRR, 2025

2025
[18]

VLP: vision language planning for autonomous driving,

C. Pan, B. Yaman, T. Nesti, A. Mallik, A. G. Allievi, S. Velipasalar, and L. Ren, “VLP: vision language planning for autonomous driving,” inIEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, CVPR 2024, Seattle, WA, USA, June 16-22, 2024. IEEE, 2024, pp. 14 760–14 769

2024
[19]

nuscenes: A multimodal dataset for autonomous driving,

H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June...

2020
[20]

Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,

Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y . Qiao, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” inComputer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, ...

2022
[21]

Genad: Generative end-to-end autonomous driving,

W. Zheng, R. Song, X. Guo, C. Zhang, and L. Chen, “Genad: Generative end-to-end autonomous driving,” inComputer Vision - ECCV 2024 - 18th European Conference, Milan, Italy, September 29- October 4, 2024, Proceedings, Part LXV, vol. 15123. Springer, 2024, pp. 87–104

2024
[22]

Going deeper with convolutions,

C. Szegedy, W. Liu, Y . Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V . Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” inIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. IEEE Computer Society, 20...

2015
[23]

DeepMIM: deep supervision for masked image modeling,

S. Ren, F. Wei, S. Albanie, Z. Zhang, and H. Hu, “DeepMIM: deep supervision for masked image modeling,” inIEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025, Tucson, AZ, USA, February 26 - March 6, 2025. IEEE, 2025, pp. 879–888

2025
[24]

Interpretable decision-making for end- to-end autonomous driving,

M. Mirzaie and B. Rosenhahn, “Interpretable decision-making for end- to-end autonomous driving,”CoRR, vol. abs/2508.18898, 2025

2025
[25]

VLM-AD: end-to-end autonomous driving through vision-language model supervision,

Y . Xu, Y . Hu, Z. Zhang, G. P. Meyer, S. K. Mustikovela, S. Srinivasa, E. M. Wolff, and X. Huang, “VLM-AD: end-to-end autonomous driving through vision-language model supervision,”CoRR, vol. abs/2412.14446, 2024

2024 arXiv
[26]

An overview of statistical learning theory,

V . Vapnik, “An overview of statistical learning theory,”IEEE Trans. Neural Networks, vol. 10, no. 5, pp. 988–999, 1999

1999
[27]

C. M. Bishop,Pattern Recognition and Machine Learning. Springer, 2006

2006
[28]

K. P. Murphy,Machine learning - a probabilistic perspective, ser. Adaptive computation and machine learning series. MIT Press, 2012

2012
[29]

Amortized inference in proba- bilistic reasoning,

S. Gershman and N. D. Goodman, “Amortized inference in proba- bilistic reasoning,” inProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014, Quebec City, Canada, July 23-26, 2014. cognitivesciencesociety.org, 2014

2014
[30]

On information and sufficiency,

S. Kullback and R. A. Leibler, “On information and sufficiency,” Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79–86, 1951

1951
[31]

Auto-encoding variational bayes,

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Pro- ceedings, 2014. APPENDIX A. GT DATAENCODERS GT Data y TextEncoder Ztarget ❄ Prompt...

2014
[32]

6a illustrates the TE encoder used as the default in VLP

Text-based Encoder:Fig. 6a illustrates the TE encoder used as the default in VLP. GT data are first formatted into text prompts following a predefined template that differs between ALP and SLP (an example is shown in Fig. 4). The prompt is processed by a frozen TE (CLIP/RN50x6...
[33]

6b and 6c show our MLP-based encoders for ALP and SLP, respectively

MLP encoder:Figs. 6b and 6c show our MLP-based encoders for ALP and SLP, respectively. In the ALP encoder, GT agents and lanes are transformed into per-element feature vectors. Ego and agent trajectories are concatenated with a one-hot navigation command and then passed throug...
[34]

To construct a distributional target, we generate a corresponding posterior queryfrom GT annotations while condition- ing on theprior query

Prior and posterior queries:We denote the query produced by the original encoders as theprior query. To construct a distributional target, we generate a corresponding posterior queryfrom GT annotations while condition- ing on theprior query. Theposterior encoderis a fully MLP-...
[35]

Each head is a stack of linear layers with dimensions[D, 2D, 2D, D]

Source (prior) distribution:For theprior query→ priordistribution (our source distribution in the ELBO), we employ two independent MLP heads — for the mean and for the covariance. Each head is a stack of linear layers with dimensions[D, 2D, 2D, D]
[36]

Hidden dimension is set toD= 256

Implementation notes:We implement all modules in PyTorch, using linear layers withReLUactivations, and LayerNorm. Hidden dimension is set toD= 256

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Critical reasons for crashes investigated in the national motor vehicle crash causation survey,

S. Singh, “Critical reasons for crashes investigated in the national motor vehicle crash causation survey,”DOT HS, 2015

work page 2015

[2] [2]

Optimizing autonomous transfer hub networks: Quantifying the potential impact of self-driving trucks,

C. Lee, K. Dalmeijer, P. V . Hentenryck, and P. Zhang, “Optimizing autonomous transfer hub networks: Quantifying the potential impact of self-driving trucks,”EURO J. Transp. Logist., vol. 13, p. 100141, 2024

work page 2024

[3] [3]

Challenges of autonomous driving trucks and the impact on logistics,

E. Gjere, “Challenges of autonomous driving trucks and the impact on logistics,” 2023, accessed: 2025-08-30. [Online]. Available: https://www.theseus.fi/handle/10024/806084

work page 2023

[4] [4]

Autonomous high speed road vehicle guidance by computer vision,

E. Dickmanns and A. Zapp, “Autonomous high speed road vehicle guidance by computer vision,”IFAC Proceedings Volumes, vol. 20, no. 5, Part 4, pp. 221–226, 1987, 10th Triennial IFAC Congress on Automatic Control - 1987 V olume IV , Munich, Germany, 27-31 July

work page 1987

[5] [5]

Autonomous driving in urban environments: Boss and the urban challenge,

C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer,et al., “Autonomous driving in urban environments: Boss and the urban challenge,”Journal of Field Robotics, vol. 25, no. 8, pp. 425–466, 2008

work page 2008

[6] [6]

A survey of autonomous driving: Common practices and emerging technologies,

E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,” IEEE Access, vol. 8, pp. 58 443–58 469, 2020

2020

[7] [7]

ALVINN: an autonomous land vehicle in a neural network,

D. Pomerleau, “ALVINN: an autonomous land vehicle in a neural network,” inAdvances in Neural Information Processing Systems 1, [NIPS Conference, Denver, Colorado, USA, 1988]. Morgan Kaufmann, 1988, pp. 305–313

work page 1988

[8] [8]

Planning-oriented autonomous driving,

Y . Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, L. Lu, X. Jia, Q. Liu, J. Dai, Y . Qiao, and H. Li, “Planning-oriented autonomous driving,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023. IEEE, 2023, pp. 17 853–17 862

work page 2023