## 4 Evaluation Method and Simulation Requirements
### Evaluation Scenario  
Evaluation of the proposed HUD system was undertaken though a contrasting comparative study between the proposed interface and currently in use systems. Accident scenarios provided by the Strathclyde Police department to previous simulation experiments [2, 18, 53, 58] formed the basis of the potential accident simulations recreated by the system and presented to the evaluation participants. By establishing an internal consistency amongst the various trials with testing the same accident scenarios while utilising different prototype systems, we ensure a direct correlation between variables altered and evaluation result output [2, 15, 43, 50]. The simulation deemed most appropriate for this evaluation is one that employs a rear collision accident scenario. It accesses the driver's response in high-speed, low traffic situations (i.e., motorway environment) when distracted with random text messages which may or may not deviate the driver's gaze from the road and of the primary task of driving. A high-propensity collision scenario is created through fundamental AI, which controls the neighbouring agent-vehicles. The latter, computer-controlled vehicles, follow seemingly the human-driven vehicle and gradually form potential abrupt braking and path-blocking conditions following closely the collision patterns provided by the Traffic Police. In particular, the driver receives messages related to navigation or mobile phone text messages, in seemingly random intervals. In total, the driver receives four messages, two for navigation updates and two from mobile texts. The scenario presented to the  
Figure 11: Motorway M8 section from Glasgow towards Edinburgh (**a**) VR digital twin city model screenshot as can be seen when driving in the VR driving simulator, (**b**) Google maps’ screenshot of the same spot in real-life. users follows a storyline in which the driver is invited to meet friends in a location nearby Edinburgh. In this way, the user has to check the incoming navigation and text messages' trail to guide him/her to the meeting location. For the scenario version that utilises the typical HDD, the infotainment messages are presented on a tablet device positioned in the middle of the vehicle dashboard. On the second round of the experiment, the same text information is presented to the driver through the proposed AR HUD. As mentioned above, the order of the simulation runs with HDD and HUD was changed randomly for each user. In both cases, the requested task is to access and read the incoming messages from mobile phone communication and navigation updates, while driving, assuming that they feel safe to do this. The driver is intentionally enticed to interact with the incoming infotainment data as stated above by replicating a typical routine for the vast majority of the drivers with the HDD infotainment systems or with the actual mobile phone. As the simulation progresses, the division of the driver's attention secondary to the external stimulus (i.e., text message and/or navigation message) creates a situation of a potential imminent collision. In a short interval after receiving the message, a rear collision scenario is unfolding; as the lead vehicle, every time is performing an abrupt braking or full-stop braking [50; 59]. The latter is appearing on a seemingly random part of the simulation as a traffic jam is blocking the road ahead, in order to minimise the likelihood of the user memorising the events. In such a situation, the driver has three potential options, mainly related to his response time from noticing the external event, namely: break abruptly and avoid a collision, collide or circumvent the lead obstructing vehicle, and try to perform a full-stop without collision. The simulation software measures the said driver's response by calculating the response time (RT) and collision occurrence frequency in both simulation scenarios (with or without the HUD interface).