# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
###### Abstract
Driving simulators are already used in licensing exams in countries such as the United Kingdom, the Netherlands, Singapore, and Finland [27; 28; 29; 30]. Further evidence for the utility of targeted simulator training comes from research focusing on specific, high-risk manoeuvres. [PERSON] et al. [31], for instance, addressed the well-documented problem of novice drivers' poor hazard mitigation skills during left-turn scenarios, a leading cause of crashes for this demographic. To deal with this issue, they developed and evaluated a specialised training intervention in a driving simulator that focused on improving specific component skills, such as managing approach speed and accurately judging gaps in oncoming traffic. The results confirmed that such a targeted intervention significantly improved drivers' hazard mitigation strategies, underscoring the value of simulators in addressing discrete, well-defined driving deficiencies that are difficult and dangerous to train on real roads. Scientific research further suggests that high-class simulators--such as full-vehicle setups with a 180-degree field of view--are more effective for developing hazard perception and vehicle control skills compared to traditional driver training methods, and they are associated with a lower crash risk compared to traditional driver training methods [34; 35; 36; 37]. On the other hand, low-class simulators (e.g., monitor, steering wheel, and basic pedals) do not provide comparable benefits. Although no formal requirements for training drivers in the use of an ADAS have yet been developed or implemented, many researchers identify this as a key area for improving road safety [38; 39]. A critical challenge in this area is the lack of standardised tools to objectively evaluate whether a driver has achieved proficiency. This specific problem was addressed by [PERSON] et al. [7], who focused on assessing driver competence with Adaptive Cruise Control (ACC). To deal with the absence of a formal assessment framework, they developed a structured on-road evaluation method based on a set of objective Key Performance Indicators (KPIs), such as mean time headway and the frequency of unnecessary driver interventions. Their study successfully demonstrated that this data-driven approach could reliably distinguish between proficient and non-proficient users. This finding underscores the feasibility and critical importance of developing objective, metric-based assessment tools, which is a central motivation for the evaluation model presented in this paper. Studies are currently being conducted on the effectiveness of different training formats. Empirical data presented in [30] show that video-based training methods, when compared to traditional text-based manuals, significantly improve driver performance in terms of both reaction time and accuracy. Another critical area of research is the direct impact of ADASs on a driver's cognitive load and behaviour, particularly in complex, unpredictable environments. A study by [PERSON] et al. [39] investigates this problem by analysing driver performance in a simulated environment with the presence of cyclists. The authors sought to determine whether an ADAS providing warnings about speeding and dangerous curves acts as a support or an overload. Their methodology involved a driving simulator study with 14 participants, where driver behaviour was evaluated using both subjective and objective measures. They employed a Tobii Glass eye-tracker to monitor visual behaviour and the NASA-TLX questionnaire to assess the subjective workload. Their findings suggest that such an ADAS can be beneficial in moderately difficult situations but may lose effectiveness in very simple or very complex ones, highlighting the need for context-aware assistance systems. This study introduces a novel evaluation framework for ADAS training that addresses key limitations in the existing literature.