Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving
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Abstract (arXiv)
Prevailing wisdom asserts that one cannot rely on the cloud for critical real-time control systems like self-driving cars. We argue that we can, and must. Following the trends of increasing model sizes, improvements in hardware, and evolving mobile networks, we identify an opportunity to offload parts of time-sensitive and latency-critical compute to the cloud. Doing so requires carefully allocating bandwidth to meet strict latency SLOs, while maximizing benefit to the car.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 83 references that are in the map. First posted on arXiv 21 Oct 2024.
Builds on
26 of 83 references.
References · 26
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2020Center-based 3D Object Detection and Trackingcited by 1,144
- 2022Planning-oriented Autonomous Drivingcited by 1,087
- 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
- 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
- 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 397
- 2018Model compression via distillation and quantizationcited by 289
- 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 270
14 more
- 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 254
- 2016Clipper: A Low-Latency Online Prediction Serving Systemcited by 191
- 2022Compute Trends Across Three Eras of Machine Learningcited by 163
- 2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 140
- 2020Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?cited by 139
- 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 129
- 2020Serving DNNs like Clockwork: Performance Predictability from the Bottom Upcited by 120
- 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 114
- 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 101
- 2018Scenic: A Language for Scenario Specification and Scene Generationcited by 83
- 2020Learning to Evaluate Perception Models Using Planner-Centric Metricscited by 49
- 2021Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehiclescited by 33
- 2023Leveraging Cloud Computing to Make Autonomous Vehicles Safercited by 7
- 2024PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detectioncited by 3
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Citing papers per year: 2026: 1.
1 paper.