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%.

驾驶行为相关事故的避险技术对于风险缓解至关重要。我们提供了一种新的多类驾驶员分心风险评估（MDDRA）模型，该模型在旅程中考虑了车辆、驾驶员和环境数据。MDDRA 通过风险矩阵将驾驶员分类为安全、不小心或危险。我们使用 Field Operation Test（TeleFOT）收集了在英国东米德兰地区使用相同路线的真实数据。结果显示，通过减少由驾驶员分心引起的道路事故是可能的。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程度之间的相关性。此外，我们应用机器学习技术对驾驶员分心进行分类和预测，以在情况被认为存在风险时帮助控制从驾驶员转移到车辆（接管车辆）。集成袋装树算法表现最佳，准确率为 96.2%。