In recent years, advancements in autonomous driving technologies have accelerated the commercialization of highly automated trucks. This shift away from human drivers raises concerns about the loss of critical functions, particularly in post-diagnosis decision-making, which relies on human inputs in the current practice. This paper outlines the current post-diagnosis decision-making process for human-driven trucks, drawing on insights from industry practitioners, and systematically identifies gaps between these practices and the requirements for highly automated trucks. We propose a comprehensive design of an intelligent decision support system (DSS) to address these gaps. The design includes conducting a system impact analysis to identify new stakeholders, proposing a new DSS architecture with review and learning functions, and concretizing various potentially effective decision-making models and information inputs. Using a real-world freight delivery scenario and a risk-based decision-making approach, we present a case study to instantiate the DSS design, including graphical user interface designs and a step-by-step use case scenario. This work aims to adapt post-diagnosis decision-making for automated trucks at both technological and managerial levels, thereby enhancing vehicle reliability and transport efficiency.