Scenario Engineering for Autonomous Transportation: A New Stage in Open-Pit Mines
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Abstract (arXiv)
In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This research introduces a novel paradigm that integrates Scenario Engineering (SE) with autonomous transportation systems to significantly improve the trustworthiness, robustness, and efficiency in open-pit mines by incorporating the four key components of SE, including Scenario Feature Extractor, Intelligence and Index (I&I), Calibration and Certification (C&C), and Verification and Validation (V&V). This paradigm has been validated in two famous open-pit mines, the experiment results demonstrate marked improvements in robustness, trustworthiness, and efficiency. By enhancing the capacity, scalability, and diversity of autonomous transportation, this paradigm fosters the integration of SE and parallel driving and finally propels the achievement of the '6S' objectives.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 71 references that are in the map. First posted on arXiv 15 Mar 2024.
Builds on
22 of 71 references.
References · 22
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
- 2022Planning-oriented Autonomous Drivingcited by 1,068
- 2020PCT: Point cloud transformercited by 730
- 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 636
- 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
- 2022EfficientFormer: Vision Transformers at MobileNet Speedcited by 240
- 2021Mobile-Former: Bridging MobileNet and Transformercited by 234
- 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
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- 20203D Object Detection with Pointformercited by 194
- 2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
- 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
- 2022EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformerscited by 116
- 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
- 2020Why Do Better Loss Functions Lead to Less Transferable Features?cited by 73
- 2020CenterNet3D: An Anchor Free Object Detector for Point Cloudcited by 7
- 2023FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusioncited by 7
- 2022AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detectioncited by 6
- 2023Yet another Improvement of Plantard Arithmetic for Faster Kyber on Low-end 32-bit IoT Devicescited by 1
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Citing papers per year: 2024: 1.
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