mlsanityData to 7 Oct 2026

NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system

arXiv 2312.09588 ↗First posted 15 Dec 2023cs.RO cs.AIcited by 0

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Abstract (arXiv)

This paper proposes a specialized autonomous driving system that takes into account the unique constraints and characteristics of automotive systems, aiming for innovative advancements in autonomous driving technology. The proposed system systematically analyzes the intricate data flow in autonomous driving and provides functionality to dynamically adjust various factors that influence deep learning models. Additionally, for algorithms that do not rely on deep learning models, the system analyzes the flow to determine resource allocation priorities. In essence, the system optimizes data flow and schedules efficiently to ensure real-time performance and safety. The proposed system was implemented in actual autonomous vehicles and experimentally validated across various driving scenarios. The experimental results provide evidence of the system's stable inference and effective control of autonomous vehicles, marking a significant turning point in the development of autonomous driving systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 37 references that are in the map. First posted on arXiv 15 Dec 2023.

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References · 12

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  3. 2022Planning-oriented Autonomous Drivingcited by 1,068
  4. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  5. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  6. 2023FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterizationcited by 161
  7. 2022Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNscited by 155
  8. 2017Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Drivingcited by 81
  9. 2020NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Searchcited by 64
  10. 2022CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detectioncited by 30
  11. 2023Self-Aware Trajectory Prediction for Safe Autonomous Drivingcited by 5
  12. 2019A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Drivingcited by 4

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