mlsanityData to 7 Oct 2026

Understanding Time Variations of DNN Inference in Autonomous Driving

arXiv 2209.05487 ↗First posted 12 Sep 2022cs.LG cs.AI cs.ROcited by 6

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

Deep neural networks (DNNs) are widely used in autonomous driving due to their high accuracy for perception, decision, and control. In safety-critical systems like autonomous driving, executing tasks like sensing and perception in real-time is vital to the vehicle's safety, which requires the application's execution time to be predictable. However, non-negligible time variations are observed in DNN inference. Current DNN inference studies either ignore the time variation issue or rely on the scheduler to handle it. None of the current work explains the root causes of DNN inference time variations. Understanding the time variations of the DNN inference becomes a fundamental challenge in real-time scheduling for autonomous driving. In this work, we analyze the time variation in DNN inference in fine granularity from six perspectives: data, I/O, model, runtime, hardware, and end-to-end perception system. Six insights are derived in understanding the time variations for DNN inference.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 18 of its 46 references that are in the map. First posted on arXiv 12 Sep 2022.

Builds on

18 of 46 references.

References · 18

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6 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 3.

6 papers.

  1. 2025Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Timecited by 4
  2. 2023Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representationcited by 2
  3. 2023Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUscited by 0
  4. 2026All-Optical Segmentation via Diffractive Neural Networks for Autonomous Drivingcited by 0
  5. 2026Enhancing Predictability of Multi-Tenant DNN Inference for Autonomous Vehicles' Perceptioncited by 0
  6. 2026Vision-Language-Action Autonomous Driving Agent with Language-based Memorycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Imitation Learning for Autonomous Driving: Insights from Real-World Testing
  2. 2025Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
  3. 2021ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
  4. 2026Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
  5. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
  6. 2025AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving
  7. 2026The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models
  8. 2026Towards Reliable Vision-Language Models for Autonomous Driving