## 2 Materials and Methods
### Data Collection Set-Up  
A truck platooning field trial was undertaken on public rural roads in northern Norway in the fall of 2020. This is the first study of its kind, and was also reported in [23]. Three drivers operated three semi-trailer trucks along a 380 km two-way, two-lane road stretch traversing a mountainous, coastal area. The trucks were numbered 1, 2 and 3, based on the main truck order configuration. One section was traversed repeatedly with different orders. A prototype ACC system was installed, enabling the trucks to operate as a platoon when detecting a preceding truck. Data from integrated cameras and radars were unavailable. Aftermarket equipment was used to collect data over 7 h of driving. While longitudinal control was automated, the drivers operated the wheel manually, placing the field study at the Society of Automotive Engineers (SAE) Level 1 [24]. All trucks had 500 horsepower. Trucks 1 and 2 had equal weights, while truck 3 was lighter. (41 and 27.5 metric tons), reflecting the scenario of trucks encountering each other to platoon during real-world operations, unharmonized with respect to weight. Consequently, inter-vehicle distances fluctuated, as the platoon often struggled to remain collected on the winding road. Twenty-three tunnels and eleven roundabouts were traversed. The most prevalent speed limits were 80 and 60 km/h, during which inter-vehicle distances at 3-s time gaps were 50-70 m, i.e., comparable to manual driving. Figure 1 illustrates the set-up. Herein, _preceding truck_ is a term used for the truck located in front of the truck in question. Depending on the context, it may refer to either the lead truck or the middle truck. In truck order 1-2-3, truck 1 precedes truck 2, and truck 2 precedes truck 3. The terms _leader_ or _leading truck_ are only used for the truck located at the front of the platoon, while _followers_ refer to both the middle and last truck together. The two rearmost trucks were identically instrumented. The Raspberry Pi, with the uRAD sensor attached, was fixed to the inside of the windshield using a suction mount with a flexible arm. The mount did not interfere with the field-of-view of the sensors. A portable monitor was used to administer radar logging. Sensors were placed at slightly different heights in each truck due to interior constraints, cf. Appendix A.1. Two GoPro video cameras were also mounted: A windshield-mounted dashboard camera filmed the driving scene, and another camera filmed the interior. Footage from  
Figure 1: Truck platooning set-up. Radar sensors were mounted on windshields of following trucks. the latter was only used for synchronizing radar data to the dashboard footage. The study was approved by the Norwegian Centre for Research Data (457013). Participants agreed to being recorded, and all videos and audio were handled and stored confidentially. The equipment in each truck was started and stopped in succession when trucks were parked, but was left on during short breaks. None of the Raspberry Pi microprocessors had internet connection, so they did not adhere to local time. An equipment start-up procedure was devised which allowed for post-hoc synchronization of videos and radar data. For each truck, all cameras were started before starting radar logging. When starting each camera, the Emerald Sequoa Time smartphone application was presented. Using Network Time Protocol (NTP) servers, which synchronizes computer clocks over the internet [25], this application provides more accurate times than those typically provided by internal clocks [26]. The application shows local time, and, when cellular reception is available, it calculates deviations from NTP time. The mean offset was 0.08 s, i.e., negligible. GNSS data were collected from VBOX Sport loggers and a fleet management system (FMS), in an effort to compute inter-vehicle distances to validate the radar data. A script was written to interpolate timestamps and calculate distances between GNSS locations from each truck.