This paper focuses on the affective component of a driver behavioural model (DBM). This component specifically models some drivers' mental states such as mental load and active fatigue, which may affect driving performance. We have used Bayesian networks (BNs) to explore the dependencies between various relevant random variables and assess the probability that a driver is 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）中的情感成分。该成分专门模拟某些驾驶员的心理状态，如心理负荷和主动疲劳，这些状态可能影响驾驶表现。我们采用贝叶斯网络（BNs）来探索各种相关随机变量之间的依赖关系，并基于驾驶员的生理与人口统计条件评估其处于特定心理状态的概率。通过这一方法，我们的目标是增进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。