mlsanityData to 14 Oct 2026

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

arXiv 2304.14271 ↗First posted 13 Apr 2023cs.RO cs.AI cs.LGcited by 14

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Abstract (arXiv)

Autonomous driving services rely heavily on sensors such as cameras, LiDAR, radar, and communication modules. A common practice of processing the sensed data is using a high-performance computing unit placed inside the vehicle, which deploys AI models and algorithms to act as the brain or administrator of the vehicle. The vehicular data generated from average hours of driving can be up to 20 Terabytes depending on the data rate and specification of the sensors. Given the scale and fast growth of services for autonomous driving, it is essential to improve the overall energy and environmental efficiency, especially in the trend towards vehicular electrification (e.g., battery-powered). Although the areas have seen significant advancements in sensor technologies, wireless communications, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate those technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights and vision from this survey can be beneficial for the collaborative driving service development on low-power and memory-constrained systems and also for the energy optimization of autonomous vehicles.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 221 of its 439 references that are in the map. First posted on arXiv 13 Apr 2023.

Builds on

221 of 439 references.

References · 221

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  5. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  6. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  7. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  9. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  10. 2018YOLOv3: An Incremental Improvementcited by 2,659
  11. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  12. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
38 more
  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  2. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  5. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  9. 2016Pruning Filters for Efficient ConvNetscited by 1,143
  10. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  11. 2016End to End Learning for Self-Driving Carscited by 1,009
  12. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  13. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  14. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  15. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  16. 2019Relational Knowledge Distillationcited by 889
  17. 2019Towards Federated Learning at Scale: System Designcited by 832
  18. 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
  19. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  20. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 732
  21. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  22. 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 724
  23. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  24. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  25. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  26. 2016Learning Structured Sparsity in Deep Neural Networkscited by 541
  27. 2019Similarity-Preserving Knowledge Distillationcited by 516
  28. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  29. 2018PACT: Parameterized Clipping Activation for Quantized Neural Networkscited by 467
  30. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  31. 2017ThiNet: A Filter Level Pruning Method for Deep Neural Network Compressioncited by 442
  32. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  33. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  34. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
  35. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
  36. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 379
  37. 2018Sparsified SGD with Memorycited by 376
  38. 2018Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Accelerationcited by 367

50 most cited of 221 All 221 (JSON)

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Citing papers per year: 2024: 7; 2025: 5; 2026: 2.

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