Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines

TL;DR

This work tackles the difficulty of guaranteeing robustness and trustworthiness for autonomous transportation in extreme open-pit mining scenarios. It introduces Scenarios Engineering (SE), a four-component framework consisting of the Scenario Feature Extractor, Intelligence & Index ($6I$), Calibration & Certification, and Verification & Validation, to improve data quality, learning, and real-world alignment. Central to SE are the $6I$ and $6S$ constructs guiding intelligent ecologies, along with the $3I$ approach for data augmentation and validation, which aim to expand data magnitude/quality and ensure data security, human-centric, and ecological validation. Calibration paired with blockchain-based, non-fungible, globally unique certificates, together with rigorous V&V (supported by a 5W2H analysis), provides a trustworthy pathway for deploying autonomous mining trucks, with a future direction toward integrating foundation models to further enhance scenario-aware learning and safety.

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, capabilities, and contributions of the intelligent systems employed in autonomous transportation to align with traffic participants in the real world and ensure their performance through certification. V&V process ensures that the autonomous transportation system can be correctly implemented, while validation focuses on evaluating the ability of the well-trained model to operate efficiently in the complex and dynamic conditions of the open-pit mines. This methodology addresses the unique challenges of autonomous transportation in open-pit mining, promoting productivity, safety, and performance in mining operations.

Figures (3)

• Figure 1: The framework for scenarios engineering (SE) based autonomous transportation in open-pit mining.
• Figure 2: Intelligence $\&$ Index for data argumentation and validation in open-pit mines.
• Figure 3: 5W2H for autonomous transportation in open-pit mines.