## 2 Materials and Methods
### Radar Data Processing  
Video coding events were exported from BORIS as spreadsheets, and the _Date and time_ from the corresponding _observation_ was added to the timestamp of each instance of the _Radar logging_ video code. This assigned local time to the instance when radar logging was started, and served as basis for synchronizing video codes and radar data in Python. Filters were needed to extract only the inter-vehicle distances between the platooning trucks. Filtering aimed at removing data from periods when the trucks were not driving (i), data which did not correspond to the preceding truck (ii), noise (iii), and finally, data from periods when the preceding truck was outside sensor range (iv). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Context** & **Description** \\ \hline
1 & & Start GoPro-cameras successively, while, for each camera, presenting local time on phone screen. \\ \hline
2 & & Start radar logging script while producing loud verbal cue. \\ \hline
3 & Data collection & Platoon driving. \\ \hline
4 & Equipment logging stop & Stop GoPro camera recordings successively. Stop radar logging. \\ \hline
5 & Data transfer & Import GoPro video files and raw radar files to computer. \\ \hline
6 & & For each truck: Synchronize GoPro video footage in BORIS, using offset values. Synchronization is based on the difference between local time presented to each camera upon starting the recordings, and fine-tuned using recorded audio. \\ \hline
7 & & Define _Date and time_ in BORIS observation equal to the local time shown to the reference camera (i.e., the longest video file) by the phone application when the reference recording was started. \\ \hline
8 & Video coding & Code _Radar logging_ based on visual and verbal cues from interior camera. Code remaining events from dashboard camera footage. \\ \hline
9 & & Export events list for each observation to spreadsheets. \\ \hline
10 & & Synchronize radar data to local time \\ \hline
11 & Radar data curation & Radar data were curated using six filters. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the data collection and processing steps. Timestamps, distance (m), relative velocity (km/h) and signal-to-noise ratio (decibel, dB) were logged, for up to five simultaneously detected targets. Positive relative velocities corresponded to targets receding from the radar, and negative relative velocities corresponded to approaching targets. For the curated radar dataset, an example of the former would be the preceding truck accelerating away from the truck in question. Conversely, the preceding truck decelerating would be an example of the latter. SNR denotes the ratio of the signal power to the noise power. Larger and more reflective objects will produce measurements with higher SNR values. The radar data were curated using successive filters, cf. Table 2. The following paragraph outlines the details and purpose of each filter. First, the _Driving_ video code was used to remove data collected during irrelevant periods. It discards data from _Break_ periods, so only data from _Driving_ periods remain. Simultaneously, truck order codes were used to exclude radar data collected during periods when each truck served as platoon leader. Specifically, radar data from truck 2 stem from the driving periods with truck orders _1_-_2_-_3_, _1_-_3_-_2_ and _3_-_2_-_1_, while radar data from truck 3 stem from periods coded as _1_-_2_-_3_, _1_-_3_-_2_ and _2_-_1_-_3_. Data were discarded from periods where external vehicles (_Other vehicle_) preceded each respective truck. Filters for relative velocity and SNR (filters 2 and 3, respectively), were used to clean the remaining data. Relative velocities were explored in histograms, shown in Figure 4a. Some relative velocity bins were far more frequent than others, giving the dataset large dynamic range. The vertical axis is logarithmic, magnifying bins with few measurements.