## 2 Materials and Methods
### Video Footage, Synchronization and Manual Video Coding  
Dashboard footage was recorded for exploring the radar data as a function of the driving scene. Without the videos, this would not have been possible. First, footage had to be coded, i.e., timestamps had to be established for relevant events in the footage. This is different from the more computationally complex process of semantic segmentation used in computer vision, which involves categorizing relevant objects in the scene, often using bounding boxes [31]. Video footage was synchronized and aligned to local time using BORIS, i.e., Behavioral Observation Research Interactive Software [35]. BORIS is a free, open-source video coding program. Each BORIS observation contained videos from the same truck and driving stretch. By checking time differences as displayed by the phone application at the start of each recording, time offsets were established, achieving near-perfect synchronization. The date and time of each observation was defined as the local time shown by the phone application to the longest video file, as the recording was started. This ensured that all videos from the trucks were aligned to local time during the field trial. Events in BORIS were defined using the ethnogram, and were either point or state events: Point events had no duration (i.e., having only one timestamp), while state events did (i.e., having both start and end timestamps). Video coding was carried out while playing videos at 2-4 times normal speed, depending on driving scene complexity. Events were coded by the first author, ensuring consistency. Videos codes were subsequently reviewed by the third author. Onwards, _italics_ are used to refer to the video codes, as illustrated in Figure 3. Radar initiations (_Radar logging_) and oncoming traffic were defined as point events. Amongst oncoming vehicles, only _Trucks_ seemed to affect inter-vehicle distances during platooning. Video footage showed that when encountering large trucks on narrow road segments, the lead truck often reduced its speed, causing speed reductions also for the followers and a contraction in inter-vehicle distances. Truck order codes indicate which periods the instrumented trucks collected relevant inter-vehicle distance data within the platoon, as opposed to the periods where they served as platoon leaders, collecting irrelevant data preceding the platoon. The _Driving_ video code in Figure 3 includes all conditions encountered, i.e., including tunnels and roundabouts, thus showcasing diverse, complex driving segments. See Table 15 in Appendix.2 for its definition. _Tunnel_ and _Roundabout_ video codes denote scenarios of particular interest. In tunnels, inter-vehicle distances between the trucks cannot be determined using GNSS-based methods, and it is also unclear how tunnels affect operating conditions for the radar sensors. Roundabouts are demarcated areas (i.e., they are simple to code from video footage) with small horizontal radii, which can illustrate effects of road curvature on radar operation when the preceding truck turns. All events were coded separately for each truck. Tunnels were coded from the moment when the front of the truck in question entered the tunnel, to when the front of the truck left the tunnel. The same principle was used for roundabouts, i.e., coding the moment when the front of each truck entered and exited the circulating area. Events were defined for visual inspection of the distance to (visibility of) the preceding truck, as the trucks were at times located far apart. The goal was to remove data from periods when the preceding truck was difficult or impossible for the sensor to detect, due to the driving situation. This occurred in two scenarios. Firstly, it occurred in sharp turns, where the preceding truck would disappear from radar field-of-view. Dashboard cameras had larger horizontal fields-of-view than radar sensors, so when no preceding vehicle appeared on camera, the radar would also not detect it. Secondly, it occurred when the trucks drove far apart.