Risk mitigation techniques are critical to avoiding accidents associated with driving behaviour. We provide a novel Multi-Class Driver Distraction Risk Assessment (MDDRA) model that considers the vehicle, driver, and environmental data during a journey. MDDRA categorises the driver on a risk matrix as safe, careless, or dangerous. It offers flexibility in adjusting the parameters and weights to consider each event on a specific severity level. We collect real-world data using the Field Operation Test (TeleFOT), covering drivers using the same routes in the East Midlands, United Kingdom (UK). The results show that reducing road accidents caused by driver distraction is possible. We also study the correlation between distraction (driver, vehicle, and environment) and the classification severity based on a continuous distraction severity score. Furthermore, we apply machine learning techniques to classify and predict driver distraction according to severity levels to aid the transition of control from the driver to the vehicle (vehicle takeover) when a situation is deemed risky. The Ensemble Bagged Trees algorithm performed best, with an accuracy of 96.2%.

翻译：风险管理技术对于避免与驾驶行为相关的事故至关重要。我们提出了一种新颖的多类驾驶员分心风险评估（MDDRA）模型，该模型在行驶过程中综合考虑车辆、驾驶员和环境数据。MDDRA将驾驶员分为安全、粗心或危险三个风险等级。该模型具有灵活性，可调整参数和权重以匹配每个事件的具体严重程度。我们通过实地操作测试（TeleFOT）收集了英国东米德兰兹地区相同路线驾驶员的真实数据。结果表明，减少因驾驶员分心导致的道路事故是可行的。我们还研究了分心（驾驶员、车辆和环境）与基于连续分心严重程度评分的分类严重性之间的相关性。此外，我们应用机器学习技术根据严重程度对驾驶员分心进行分类和预测，以便在判定为高风险情况时辅助从驾驶员到车辆的操控权过渡（车辆接管）。集成袋装树算法表现最佳，准确率达到96.2%。