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

• URL: http://arxiv.org/abs/2605.29138v1
• Date: Wed, 27 May 2026 22:06:23 GMT
• Title: Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
• Abstract summary: In autonomous driving, safety depends on both prediction quality and the end-to-end delay from sensing to actuation.<n>We present a multi-resolution, end-to-end deep neural network for the CARLA urban driving challenge using monocular camera input.<n>Results show consistent improvements in per-route safety metrics.
• Score: 2.134211474877041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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