## 4 Evaluation Method and Simulation Requirements
### Digital Twin City and AI  
The VR driving simulator follows on previous successful versions designed for this type of evaluations, yet is improved on graphics realism, artificial intelligence (AI) of the computer-controlled vehicles, and the physics fidelity provided [2; 15; 49]. Typical, driver's behavioural patterns were scripted for individual AI vehicles which allowed them to react and manoeuvre around a user driver [50; 51]. This facilitates seamless interaction between the driver and the suspension of disbelief with regards to the user driver and also stimulates user concentration as they are forced to react to the actions of the AI-controlled vehicular agents. The number of AI vehicles and speeds replicated a typical midday traffic flow during the week. Fluctuations on speed and driving patterns were incorporated to resemble closely real traffic [50; 51; 52; 53; 54]. The driving patterns utilised for the AI vehicles were provided through consultation with the Strathclyde Traffic Police department [2; 19; 50; 51; 52]. The particular patterns covered an array of drivers, vehicle types, and styles appearing on the UK motorways [54]. In addition, the research group retrieved traffic network information, collision scenarios, and statistics through consultation with the Road Safety Framework team and Road Safety Policy team within Transport Scotland [53]. Different virtual persona were created for the AI-driven vehicles, reflecting the information provided by the aforementioned organisations. Additionally, these AI-controlled vehicles would create the conditions to engage the user in an accident event as described by the local traffic police [2; 52]. The latter information is used to simulate the accident scenarios in the precise position that these typically occur in the real-city motorways. The arrival of the driver to the accident occurring zone depends upon previous potential accidents that might occur and the driver's speed fluctuations. The current design has several properties which can be configured to determine the behaviour of how an AI vehicle travels on the road. These include the speed, the line of travel, and the variation in the line of travel. Approaching an area that requires a driving behaviour divergent from what is normally employed, the AI vehicles will react depending on the proximity and information provided by the trigger area as illustrated in Figure 9. Within this area, the vehicles attempt to create an abrupt-breaking of the lead AI vehicle situation which will challenge the user's response. Prior to some of these challenges, the user receives mobile phone messages or navigational information that intend to distract him/her from the main task. A description of the evaluation scenarios is presented in detail in the following Section 4.3. To immerse the driver further, a digital twin of the city of Glasgow's surrounding motorways has been modelled. The selected routes of M8, M73, and M80 formed a 28 miles triangle which is the busiest part of the road-network connecting Glasgow to Edinburgh and Stirling whilst offering the path to the north in Highlands and to the south towards North England as depicted in Figure 10.  
Due to the size and complexity of the area, the team opted to model only the visible buildings and the landmarks alongside the motorways. For the simulation purposes, the traffic flow of the digital twin model was asynchronous to replicate the particular conditions that lead to an accident. A synchronous version would have been unusable as predetermined events could not be embedded. As such an instance of the digital twin model representing a typical flow during daytime was utilised for the experiment. As such, the digital twin city section offers a multilayered model that entails architectural information, AI traffic flow, road network, and VANETS simulation data, encapsulating multiple elements that could affect the mobility on the road network [3; 49; 52; 53; 54].