In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. \textcolor{red}{Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the Multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives.} \textcolor{red}{The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.

翻译：近年来，智能车辆的路径规划领域取得了显著进展。然而，作为典型的非结构化环境，露天矿区因其复杂的作业条件和不利环境因素而受到较少关注。本研究提出了一种面向露天矿区无人运输的综合范式，包括仿真平台、测试基准以及一个可信且鲁棒的运动规划器。首先，我们通过多传感器融合方法提出了一种名为FusionPlanner的多任务运动规划算法，用于自动驾驶矿用卡车，以适配无人运输的横向和纵向控制任务。其次，我们开发了一个名为MiningNav的新型基准，该基准提供三种验证方法，用于评估训练好的算法在露天矿区运输道路上的可信度和鲁棒性。最后，我们介绍了平行采矿模拟器（PMS），这是一个专为露天采矿场景设计的高保真模拟器。PMS使用户能够从单车控制和多车调度两个角度管理露天矿区的运输任务。FusionPlanner的性能通过MiningNav在PMS中进行了测试，实证结果表明，我们的规划器显著减少了碰撞和接管次数。我们预期，这一无人运输范式将使矿用卡车在连续全天候无人运输中更接近可信与鲁棒的目标。