## 4 Conclusions  
Anteral uRAD radar sensors for Raspberry Pi were tested for estimating inter-vehicle distances between trucks. Three trucks participated in a real-word platooning field study. Data from integrated sensors were unavailable. Comparable results were found from the sensors in the two rearmost trucks, suggesting that they are feasible for this use case. Data filtering involved a multi-faceted methodology. While also filtering based on relative velocity and signal-to-noise ratio, video footage allowed for removal of data from irrelevant periods, and for exploring sensor operation in roundabouts and tunnels. This would not have been possible without video footage. The curated radar data can be used to model expected fuel savings from truck platooning on specific types of roads and road features.  
Sensor ranges were estimated at 74 and 71 m, i.e., slightly shorter than suggested by theoretical calculations. The sensors captured the preceding truck for 83-85% of the time when it was located within radar range. In tunnels specifically, 95-96% of driving time was accounted for, likely due to closer driving. Average SNR decreased 3% in tunnels, compared to all driving, but this did not appear to affect the accuracy of the target detection step. When turning in roundabouts, the field-of-view to the preceding truck was often lost, and the sensors detected their surroundings until field-of-view was regained after completing the turn, causing average SNR values to drop (30-32% lower). Straight movements in roundabouts were less problematic, as field-of-view was mostly retained.  
High SNR values were observed at far distances, indicating that the sensors, with optimal parameters, may be capable of measuring preceding trucks further away. The findings suggest that simple, inexpensive radar sensors and action cameras can facilitate collection of inter-vehicle distance data from truck platooning field trials.  
**Author Contributions:** Conceptualization, M.M.L.; methodology, M.M.L., T.T. ([PERSON]), M.H.R.E.; software, T.T. ([PERSON]); validation, T.T. ([PERSON]); formal analysis, M.M.L.; investigation, M.M.L.; data curation, T.T. ([PERSON]); writing--original draft preparation, M.M.L.; writing--review and editing, M.M.L., M.H.R.E., T.T. ([PERSON]), T.L.; visualization, T.T. ([PERSON]); supervision, T.T. ([PERSON]), T.L.; project management, M.M.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the Norwegian University of Science and Technology (NTNU) and the Norwegian Public Roads Administration (NPRA), through the Innovation and Implementation initiative (D11351). The field study itself, titled \"Smart and Connected Truck Train\" was financed by the NPRA, cf. the Norwegian database for public procurement. [[https://doffin.no/nn/Notice/Details/2020-301190](https://doffin.no/nn/Notice/Details/2020-301190)]([https://doffin.no/nn/Notice/Details/2020-301190](https://doffin.no/nn/Notice/Details/2020-301190)).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all participants involved.  
**Data Availability Statement:** Data are available on request from the corresponding author.  
**Acknowledgments:** The main author would like to acknowledge the Anteral supplier team for answering inquiries.  
**Conflicts of Interest:** The authors declare no conflict of interest.