Maintaining adequate situation awareness (SA) is crucial for the safe operation of conditionally automated vehicles (AVs), which requires drivers to regain control during takeover (TOR) events. This study developed a predictive model for real-time assessment of driver SA using multimodal data (e.g., galvanic skin response, heart rate and eye tracking data, and driver characteristics) collected in a simulated driving environment. Sixty-seven participants experienced automated driving scenarios with TORs, with conditions varying in risk perception and the presence of automation errors. A LightGBM (Light Gradient Boosting Machine) model trained on the top 12 predictors identified by SHAP (SHapley Additive exPlanations) achieved promising performance with RMSE=0.89, MAE=0.71, and Corr=0.78. These findings have implications towards context-aware modeling of SA in conditionally automated driving, paving the way for safer and more seamless driver-AV interactions.

翻译：维持充分的态势感知对于条件自动化车辆的安全运行至关重要，这要求驾驶员在接管事件中重新获得控制权。本研究利用模拟驾驶环境中收集的多模态数据（如皮肤电反应、心率、眼动追踪数据及驾驶员特征），开发了一种用于实时评估驾驶员态势感知的预测模型。六十七名参与者经历了带有接管事件的自动化驾驶场景，这些场景在风险感知和自动化错误存在性方面有所不同。基于SHAP识别的12个最优预测因子训练的LightGBM模型取得了显著性能：RMSE=0.89，MAE=0.71，Corr=0.78。这些发现为条件自动化驾驶中情境感知的态势感知建模提供了启示，为更安全、更顺畅的驾驶员与自动驾驶车辆交互铺平了道路。