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arxiv: 2605.29138 · v1 · pith:XHT2RQZFnew · submitted 2026-05-27 · 💻 cs.RO · cs.AI· cs.LG· cs.SY· eess.SY

Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving

Qitao Weng , Heechul Yun This is my paper

Pith reviewed 2026-06-29 11:20 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.LGcs.SYeess.SY
keywords multi-resolution CNNautonomous drivinglatency-accuracy tradeoffCARLA simulatorend-to-end learningbatch normalizationruntime adaptation
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The pith

A multi-resolution CNN selects input scale at runtime to improve safety metrics under a latency budget in autonomous driving.

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

The paper establishes that the latency-optimal input resolution for a driving network changes with scene context and available compute, making any single fixed-resolution model suboptimal. It introduces an end-to-end CNN for the CARLA simulator that supports multiple resolutions through per-resolution batch normalization, permitting runtime selection of the best scale within a given latency limit. Resolution retargeting allows the network to be trained across resolutions even without access to the original training dataset. When evaluated on CARLA routes, the approach produces consistent reductions in lane invasions, red-light infractions, and collisions relative to fixed-resolution baselines.

Core claim

A convolutional neural network equipped with per-resolution batch normalization and resolution retargeting can choose its input resolution dynamically at inference time to respect a latency budget, resulting in lower rates of lane invasions, red-light violations, and collisions across CARLA driving routes than any single fixed-resolution counterpart.

What carries the argument

per-resolution batch normalization, which normalizes activations separately for each supported input resolution to support stable training and runtime scale selection

Load-bearing premise

Per-resolution batch normalization combined with resolution retargeting enables effective multi-resolution training and runtime selection without introducing significant accuracy loss or training instability compared to standard single-resolution training.

What would settle it

Evaluating the multi-resolution model on identical CARLA routes against the strongest fixed-resolution baseline and observing no reduction in lane invasions, red-light infractions, or collisions.

Figures

Figures reproduced from arXiv: 2605.29138 by Heechul Yun, Qitao Weng.

Figure 1
Figure 1. Figure 1: End-to-end policy overview. A CNN backbone encodes the camera [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Baseline network architecture Our only modification to the policy network is to the con￾volutional backbone (ResNet-34), which we extend to support multiple input scales via per-resolution batch normalization, as described below. Note that the segmentation head is also extended to support multiple input scales, but it is only used during training for auxiliary learning as in the original setup. A. Per-Reso… view at source ↗
Figure 3
Figure 3. Figure 3: Traffic-light infractions vs. input scale at a 50 ms control period, [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Lane invasions per route (mean±sd) at a 50 ms control period, with/without 50 ms injected delay. E. Runtime Latency-Accuracy Tradeoffs In this experiment, we investigate the potential benefits of our approach by dynamically switching resolutions depending on the environment compared to the fixed resolution baselines. Here, we assume an “oracle”, which decides which reso￾lution is ideal for a given environm… view at source ↗
Figure 5
Figure 5. Figure 5: Success and collision rates vs. injected delay ( [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Latency-accuracy tradeoffs are fundamental in real-time applications of deep neural networks (DNNs) for cyber-physical systems. In autonomous driving, in particular, safety depends on both prediction quality and the end-to-end delay from sensing to actuation. We observe that (1) when latency is accounted for, the latency-optimal network configuration varies with scene context and compute availability; and (2) a single fixed-resolution model becomes suboptimal as conditions change. We present a multi-resolution, end-to-end deep neural network for the CARLA urban driving challenge using monocular camera input. Our approach employs a convolutional neural network (CNN) that supports multiple input resolutions through per-resolution batch normalization, enabling runtime selection of an ideal input scale under a latency budget, as well as resolution retargeting, which allows multi-resolution training without access to the original training dataset. We implement and evaluate our multi-resolution end-to-end CNN in CARLA to explore the latency-safety frontier. Results show consistent improvements in per-route safety metrics - lane invasions, red-light infractions, and collisions - relative to fixed-resolution baselines.

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

1 major / 1 minor

Summary. The manuscript proposes a multi-resolution end-to-end CNN for the CARLA urban driving task that uses per-resolution batch normalization and resolution retargeting to support runtime input-scale selection under latency constraints. It claims that this architecture yields consistent improvements in per-route safety metrics (lane invasions, red-light infractions, collisions) relative to fixed-resolution baselines.

Significance. If the multi-resolution model truly matches or exceeds the per-scale accuracy of dedicated single-resolution models, the approach would provide a practical mechanism for context-aware latency-accuracy adaptation in real-time cyber-physical systems. The resolution-retargeting technique for training without the original dataset is a pragmatic engineering contribution.

major comments (1)
  1. [Abstract] The headline safety-metric gains rest on the assumption that the shared multi-resolution model, when evaluated at any chosen scale, performs at least as well as a model trained from scratch at that exact scale. No ablation, per-resolution accuracy table, or direct comparison of multi-resolution weights against single-resolution counterparts at identical input resolutions is supplied; without this evidence the observed gains cannot be attributed to the multi-resolution capability rather than unequal training effort or baseline construction.
minor comments (1)
  1. The abstract states that results were obtained in CARLA but provides no information on the number of routes, evaluation protocol, statistical significance, or variance across runs.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. The major comment correctly identifies a missing comparison that is needed to strengthen the attribution of results to the multi-resolution design. We will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] The headline safety-metric gains rest on the assumption that the shared multi-resolution model, when evaluated at any chosen scale, performs at least as well as a model trained from scratch at that exact scale. No ablation, per-resolution accuracy table, or direct comparison of multi-resolution weights against single-resolution counterparts at identical input resolutions is supplied; without this evidence the observed gains cannot be attributed to the multi-resolution capability rather than unequal training effort or baseline construction.

    Authors: We agree that a direct per-resolution comparison is required to substantiate the claims. The current manuscript does not include an ablation table contrasting the multi-resolution model (evaluated at each scale) against dedicated single-resolution models trained from scratch at the same resolutions. In the revised manuscript we will add this ablation, reporting lane invasions, red-light infractions, and collisions for both the shared model and the scale-specific baselines at each input resolution. This will allow readers to verify that performance is preserved or improved by the multi-resolution architecture (including per-resolution batch normalization) rather than differences in training effort. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical evaluation of multi-resolution architecture

full rationale

The paper proposes a multi-resolution CNN using per-resolution batch normalization and resolution retargeting, then evaluates it empirically in the CARLA simulator against fixed-resolution baselines. All load-bearing claims (safety metric improvements) rest on reported simulator runs rather than any derivation, fitted parameter renamed as prediction, or self-citation chain. No equations, uniqueness theorems, or ansatzes are invoked that could reduce to the inputs by construction. The method is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review based solely on abstract; no free parameters, axioms, or invented entities are specified in the provided text.

pith-pipeline@v0.9.1-grok · 5740 in / 1008 out tokens · 33038 ms · 2026-06-29T11:20:19.795365+00:00 · methodology

discussion (0)

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