Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
Researchers propose a multi-resolution end-to-end deep neural network to balance latency and safety in autonomous driving. By selecting input resolution at runtime, the network improves safety metrics like lane invasions, red-light infractions, and collisions in CARLA simulations compared to fixed-resolution baselines.
Article intelligence
Key points
- Latency-accuracy tradeoff is critical for real-time autonomous driving decisions.
- Proposed multi-resolution CNN supports runtime input resolution selection under latency budgets.
- Per-resolution batch normalization enables multi-resolution training without original dataset.
- Evaluation in CARLA shows consistent safety improvements over fixed-resolution baselines.
Why it matters
This matters because latency-accuracy tradeoff is critical for real-time autonomous driving decisions.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.29138] Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
[Submitted on 27 May 2026]
Title:Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
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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.
Comments: ICCPS 2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2605.29138 [cs.RO]
(or arXiv:2605.29138v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.29138
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: QiTao Weng [view email] [v1] Wed, 27 May 2026 22:06:23 UTC (933 KB)
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