  Planning module is an essential component of intelligent vehicle study. In
this paper, we address the risk-aware planning problem of UGVs through a
global-local planning framework which seamlessly integrates risk assessment
methods. In particular, a global planning algorithm named Coarse2fine A* is
proposed, which incorporates a potential field approach to enhance the safety
of the planning results while ensuring the efficiency of the algorithm. A
deterministic sampling method for local planning is leveraged and modified to
suit off-road environment. It also integrates a risk assessment model to
emphasize the avoidance of local risks. The performance of the algorithm is
demonstrated through simulation experiments by comparing it with baseline
algorithms, where the results of Coarse2fine A* are shown to be approximately
30% safer than those of the baseline algorithms. The practicality and
effectiveness of the proposed planning framework are validated by deploying it
on a real-world system consisting of a control center and a practical UGV
platform.
