Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus delivers adaptive human machine interaction with reduced impact on the primary task of driving. In this paper, we tackle the problem of workload estimation from driving performance data. First, we present a novel on-road study for collecting subjective workload data via a modified peripheral detection task in naturalistic settings. Key environmental factors that induce a high mental workload are identified via video analysis, e.g. junctions and behaviour of vehicle in front. Second, a supervised learning framework using state-of-the-art time series classifiers (e.g. convolutional neural network and transform techniques) is introduced to profile drivers based on the average workload they experience during a journey. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload. This computationally efficient and flexible method can be easily personalised to a driver (e.g. incorporate their inferred average workload profile), adapted to driving/environmental contexts (e.g. road type) and extended with data streams from new sources. The efficacy of the presented profiling and instantaneous workload estimation approaches are demonstrated using the on-road study data, showing $F_{1}$ scores of up to 92% and 81%, respectively.

翻译：监控驾驶员的脑力工作负荷有助于促进与车载信息系统的安全交互，从而在减少对驾驶主任务影响的前提下实现自适应人机交互。本文针对利用驾驶性能数据进行工作负荷估计的问题展开研究。首先，我们提出了一项创新的道路实测研究，通过在自然场景中改进的周边探测任务收集主观工作负荷数据。通过视频分析识别诱发高脑力负荷的关键环境因素，如交叉路口及前车行为。其次，引入基于前沿时间序列分类器（如卷积神经网络与变换技术）的监督学习框架，根据驾驶员在行程中经历的平均工作负荷进行驾驶员画像。然后提出一种贝叶斯滤波方法，用于（准）实时顺序估计驾驶员的瞬时工作负荷。该计算方法高效灵活，可便捷地实现驾驶员个性化（例如融合其推断的平均工作负荷画像）、适应驾驶/环境情境（如道路类型），并可扩展至新数据源的信息流。通过道路实测数据验证了所提出的画像方法与瞬时负荷估计方法的有效性，其$F_{1}$分数分别高达92%与81%。