  New autonomous driving technologies are emerging every day and some of them
have been commercially applied in the real world. While benefiting from these
technologies, autonomous trucks are facing new challenges in short-term
maintenance planning, which directly influences the truck operator's profit. In
this paper, we implement a vehicle health management system by addressing the
maintenance planning issues of autonomous trucks on a transport mission. We
also present a maintenance planning model using a risk-based decision-making
method, which identifies the maintenance decision with minimal economic risk of
the truck company. Both availability losses and maintenance costs are
considered when evaluating the economic risk. We demonstrate the proposed model
by numerical experiments illustrating real-world scenarios. In the experiments,
compared to three baseline methods, the expected economic risk of the proposed
method is reduced by up to $47\%$. We also conduct sensitivity analyses of
different model parameters. The analyses show that the economic risk
significantly decreases when the estimation accuracy of remaining useful life,
the maximal allowed time of delivery delay before order cancellation, or the
number of workshops increases. The experiment results contribute to identifying
future research and development attentions of autonomous trucks from an
economic perspective.
