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.Comment: 2Pages, 10 figure