This paper focuses on the affective component of a Driver Behavioural Model (DBM), specifically modelling some driver's mental states, such as mental load and active fatigue, which may affect driving performance. We used Bayesian networks (BNs) to explore the dependencies between various relevant variables and estimate the probability that a driver was in a particular mental state based on their physiological and demographic conditions. Through this approach, our goal is to improve our understanding of driver behaviour in dynamic environments, with potential applications in traffic safety and autonomous vehicle technologies.

翻译：本文聚焦于驾驶员行为模型（DBM）的情感成分，特别针对可能影响驾驶表现的某些驾驶员心理状态（如心理负荷与主动疲劳）进行建模。我们采用贝叶斯网络（BNs）探究各相关变量间的依赖关系，并依据驾驶员的生理与人口统计学条件估算其处于特定心理状态的概率。通过该方法，我们的目标是深化对动态环境中驾驶员行为的理解，该研究在交通安全与自动驾驶技术领域具有潜在应用价值。