## 2 Materials and Methods
### Radar Data Processing
\begin{table}
\begin{tabular}{c l} \hline \hline
**Filter** & **Description** \\ \hline
1 & _Driving_ and _following_ \\ \hline
2 & Relative velocity within \(\pm\)30 km/h \\ \hline
3 & Signal-to-noise ratio \textless{} 15 dB \\ \hline
4 & Target selection \\ \hline
5 & Downsampling 1 Hz \\ \hline
6 & _Likely within radar range_ (_LWRR_) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of radar data filters. Figure 4: (**a**) Relative velocity histograms. (**b**) Distance histograms with relative velocity filters. The histograms show two data spikes. Relative to a forward-facing sensor mounted in a moving truck, stationary surroundings have negative speed comparable to the speed of the truck. Assuming traffic moves at the speed limit, oncoming vehicles are measured with negative relative velocities at twice the speed limit. Similarly, measurements from the preceding truck have relative velocities fluctuating around 0 km/h. These two clusters of relative velocities appear as vertical spikes in Figure 4a. The cluster from the preceding truck was the largest. This is as expected in car-following situations, which necessitate continuous acceleration and deceleration [37]. The smaller clusters in Figure 4a had an order of magnitude fewer measurements, all of which had relative velocities in the \(-30\) to \(-160\) km/h range. They included static objects, oncoming traffic and measurement noise. Thus, for the relative velocity filter (filter 2), choosing \(-30\) km/h as the lower threshold was natural, placing it at the local minimum between the spikes. Similarly, setting the upper threshold at +30 km/h made it so the entire top spike was included, while minimizing the inclusion of measurements from the noise floor. As shown in Figure 4b, the radar data were also subjected to different relative velocity filters, starting at \(\pm 40\) km/h, successively constricting by \(\pm 10\) km/h steps until the narrowest filter of \(\pm 10\) km/h. The color of each filter reflects remaining data points _after_ that filter has been applied. For instance, remaining data after relative velocity filtering at \(\pm 30\) km/h are shown in red. The \(\pm 40\) km/h filter left a spike from 10-20 m for truck 3. The \(\pm 30\) km/h filter removed most of the spike, and subsequent filter constriction did not cause notable differences. Thus, \(\pm 30\) km/h was chosen, striking a balance between retaining most measurements corresponding to the preceding trucks and minimizing unwanted ones (oncoming and stationary objects), while including situations with sudden braking and acceleration in the platoon, which are perhaps the most interesting ones from a safety and fuel savings perspective. The radar sensors were listed as having a velocity accuracy of \(\pm 0.25\) m per second. At \(\pm 30\) km/h cut-offs, this amounts to a possible deviation of 3%, which is considered acceptable. Still, as shown in Figure 4b, relative velocity filtering did not remove the leftmost spikes of measurements at distances too short to represent the preceding truck (0-5 m). Since the platoon drove with 2-3 s gaps, these do not represent the preceding truck. Calculations using Equation (A1), based on the 30\({}^{\circ}\) vertical field-of-view of the sensor, show that the road can be detected at 8-8.5 m forwards, meaning further away than the spikes. Thus, they are most likely clutter due to roadside detections, such as such as rock faces, tunnel walls, guardrails and signposts. Compared to the large, reflective rear walls of preceding trucks, such measurements should presumably be noisy, i.e., have small SNR values. Conversely, if these measurements did originate from the preceding truck, they should have had large accompanying SNR values. SNR values at short distances were indeed found to be small, and filtering for SNR < 15 dB was successful in removing them. As shown in Figure 5a, 15 dB filtering fell at a local minimum or saddle point between two distinct SNR data spikes.