Signal processing based automated road condition surveys (ARCS) system are the solution for the current unsafe, subjective and labor-intensive manual road condition surveys. Although extensive research has been conducted on methods for ARCS, application by transportation agencies is still minimal. In 2016, an ARCS system, developed by Georgia Tech, was successfully implemented on a 4,184km highway system in Georgia, USA. This paper presents the insights gained from the project and also discusses the remaining challenges with a focus on crack detection and classification. Crack fundamental elements were implemented to obtain a flexible multi-scale output. A combination of ARCS and QA/QC tools were used to obtain high accuracy results while minimizing human effort. Gaps in ARCS research, such as the lack of a crack detection algorithm performance measure were revealed. The solutions and new challenges revealed from this study will help ARCS researchers to create solutions which can be readily applied by transportation agencies.