## 3 Results and Discussion
### Suggestions for Future Work  
This study explored the extent to which uRAD radar sensors could capture inter-vehicle distances during truck platooning. Several suggestions have been identified.  
First, mode 4 (7 Hz) might be more suitable than mode 3 (13 Hz). As data were downsampled anyway, the trade-off between higher update rate (mode 3) and enhanced properties for ghost target reductions in complex scenarios (mode 4) should be explored.  
To simplify data collection, radars could be remotely engaged from escort vehicles. If field trials are undertaken in areas with adequate cell coverage, virtual network computing (VNC) could be used to remote control all Raspberry Pi microprocessors. Radar data may also be visualized and coded in real-time. Future work may validate the radar data against GNSS positions, if undertaken in areas with good conditions for GNSS receivers. The performance of the sensors should also be compared against a known baseline, i.e., radars with known characteristics, such as those listed in [14], in controlled environments.  
The cross-sectional signature of the trucks was not measured, and scattering effects were not explored. If present, such effects would be reflected in SNR values if systematically inspected at equal distances while varying the angle of the back wall of the preceding truck, resembling the set-up in [40]. Windshield attenuation effects were also not studied.  
Synchronization of videos and radar data, and the subsequent process of video coding, worked well. However, both should preferably be automated, to reduce post-processing  
Figure 11: Radar data from three separate driving excerpts with _Roundabouts_, _LWRR_.  
efforts and related human errors. Traffic and infrastructure events may be identified directly from radar data. Herein, tunnels and roundabouts were coded from the moment where each respective truck entered them, and to the moment when each respective truck left them. Later, video codes were overlaid on the radar data collected from each truck. However, since radar data shows the _preceding truck_, this may have introduced a systematic error. Perhaps video codes from the preceding truck should have been used instead. For tunnels, this is not particularly problematic since tunnel traversals (with _LWRR_) had long durations (on average 1.1 min). Thus, entering and leaving the tunnel occupies a very small part of the total duration. For roundabouts, however, the preceding truck had often traversed \(\frac{1}{4}-\frac{1}{2}\) of the roundabout before the truck in question entered it, and it was first coded. Distinctions may be made between the preceding truck being located beyond range, and it being located laterally beyond field-of-view, as these are different phenomena. All data points may also be given metadata for all relevant video codes, simplifying video inspections of interesting events in the data.  
Statistical approaches may allow cut-off values for filters 2 and 3 to be chosen automatically. In the target detection step, distance filtering could also be considered, perhaps discarding data points with distances deviating significantly from the general trend. This may solve the problem of erroneous single-object detections. Established data filtering, target tracking [41] or clustering techniques [42; 43; 44] could also be used, alongside more computationally complex methods for annotating or labelling combinations of radar and camera footage, e.g., in [31; 45], and perhaps also machine learning approaches [46].  
At times, dashboard cameras malfunctioned due to power issues, totaling 6% of driving time, during which 10% of all radar data were logged. As it was not possible to determine whether the sensors had reasonable operating conditions in these periods, the data were discarded. Mitigations include redundant cameras and independent power supplies.