Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines
Siyu Teng, Xuan Li, Yuchen Li, Lingxi Li, Yunfeng Ai, Long Chen

TL;DR
This paper introduces a novel scenarios engineering methodology to enhance the robustness and trustworthiness of autonomous mining trucks in open-pit mines, addressing extreme scenario challenges.
Contribution
The paper presents a comprehensive SE framework with components for feature extraction, data enhancement, certification, and validation tailored for autonomous mining trucks.
Findings
Improved robustness of autonomous trucks in complex scenarios.
Enhanced dataset quality for training autonomous systems.
Validated system performance in dynamic mining environments.
Abstract
One critical bottleneck that impedes the development and deployment of autonomous transportation in open-pit mines is guaranteed robustness and trustworthiness in prohibitively extreme scenarios. In this research, a novel scenarios engineering (SE) methodology for the autonomous mining truck is proposed for open-pit mines. SE increases the trustworthiness and robustness of autonomous trucks from four key components: Scenario Feature Extractor, Intelligence & Index (I&I), Calibration & Certification (C&C), and Verification & Validation (V&V). Scenario feature extractor is a comprehensive pipeline approach that captures complex interactions and latent dependencies in complex mining scenarios. I&I effectively enhances the quality of the training dataset, thereby establishing a solid foundation for autonomous transportation in mining areas. C&C is grounded in the intrinsic regulation,…
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