## 3 Materials and Methods
### Virtual Reality Scenarios  
The typology of urban scenarios chosen to be modelled in the VR application corresponds to the categories established in the report by TNO in the City Alternative Transport System (CATS) project [32] of the analysis of vehicle-cyclist accident scenarios in the EU [33]. Since categories C and L account for 63% of seriously injured and 78% of fatal collisions between vehicles and cyclists, both categories are included in the modeling. The On-coming category, which is the fourth most relevant category, and the T4 category are also included.  
The design process of the virtual environment is the same that the authors carried out for [28]. The reconstruction of the scenarios was conducted with Unity, incorporating all the infrastructural elements of the roadway, as well as the rest of the dynamic elements (including motorized and non-motorized users). The traffic logic and user movement was recreated in detail for each scenario. In addition, stereophonic sounds were integrated, including environmental background noise (traffic and voices), as well as engine, rolling and braking noise from vehicles. The user receives the audible stimuli through the headphones integrated in the VR headset.  
Figure 2 depicts the relative movements between the cyclist and the vehicle in each scenario, according to the typology established by the TNO report in the CATS project.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Type** & **Description** & **GPS Coordinates** \\ \hline \multirow{3}{*}{[PERSON] (SC1)} & \multirow{3}{*}{On} & The vehicle encroaches into the oncoming & \multirow{3}{*}{40\({}^{\circ}\)25\({}^{\prime}\)55.8\({}^{\prime\prime}\) N 3\({}^{\circ}\)37\({}^{\prime}\)35.8\({}^{\prime\prime}\) W} \\  & & lane when passing a double-parked & \\  & & vehicle, resulting in a head-on collision & \\  & & between vehicle and cyclist & \\ \hline \multirow{3}{*}{Av. Machupichu (SC2)} & \multirow{3}{*}{L2} & The cyclist tries to pass a bus stopped at a & \multirow{3}{*}{40\({}^{\circ}\)27\({}^{\prime}\)38.6\({}^{\prime\prime}\) N 3\({}^{\circ}\)37\({}^{\prime}\)57.7\({}^{\prime\prime}\) W} \\  & & & causes a rear-end collision. & \\ \hline \multirow{3}{*}{[PERSON] (SC3)} & \multirow{3}{*}{C2} & The vehicle skips the yield sign and & \multirow{3}{*}{40\({}^{\circ}\)25\({}^{\prime}\)55.2\({}^{\prime\prime}\) N 3\({}^{\circ}\)37\({}^{\prime}\)39.4\({}^{\prime\prime}\) W} \\  & & causes a lateral collision. & \\ \hline \multirow{3}{*}{[PERSON] (SC4)} & \multirow{3}{*}{C2} & The vehicle skips the traffic light priority & \multirow{3}{*}{40\({}^{\circ}\)25\({}^{\prime}\)47.7\({}^{\prime\prime}\) N 3\({}^{\circ}\)37\({}^{\prime}\)56.3\({}^{\prime\prime}\) W} \\  & & and causes a lateral collision. & \\ \hline \multirow{3}{*}{Av. de los Toreros (Ventas) (SC5)} & \multirow{3}{*}{T4} & Vehicle hits cyclist while backing out of & \multirow{3}{*}{40\({}^{\circ}\)25\({}^{\prime}\)55.9\({}^{\prime\prime}\) N 3\({}^{\circ}\)39\({}^{\prime}\)42.3\({}^{\prime\prime}\) W} \\  & & an angle parking space. The cyclist is in & \\ \cline{1-1}  & & the adjacent lane. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scenarios modeled in VR for the experimental session.  
Figure 1: Methodology main scheme.  
The vehicle speed is set at 27 km/h, corresponding to the average annual traffic speed in the city of Madrid in 2019 (the year in which the application was designed) [34; 35].  
The deceleration is set at 7.7 m/s2 (corresponding to the most conservative value in the braking curves of a vehicle with AEB tested on the INSIA-UPM tracks), and the driver reaction time at 1 s, according to [36].  
The above values are not applicable to SC5, since the speed and deceleration are lower when leaving the parking lot (5 km/h and 1 m/s2, respectively).