Department of Engineering Systems and Environment/Link Lab, Olsson Hall, 151 Engineer's Way, University of Virginia, Charlottesville 22904, VA, USA.
Department of Engineering Systems and Environment/Link Lab, Olsson Hall, 151 Engineer's Way, University of Virginia, Charlottesville 22904, VA, USA.
Accid Anal Prev. 2022 Jun;170:106640. doi: 10.1016/j.aap.2022.106640. Epub 2022 Mar 24.
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral patterns. Unsupervised analysis of NDD can be used to automatically detect different patterns from the driver and vehicle data. In this paper, we propose a methodology to understand changes in driver's physiological responses within different driving patterns. Our methodology first decomposes a driving scenario by using a Bayesian Change Point detection model. We then apply the Latent Dirichlet Allocation method on both driver state and behavior data to detect patterns. We present two case studies in which vehicles were equipped to collect exterior, interior, and driver behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal brake, curved driving, and highway driving), as well as two patterns of driver's heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy (i.e., low versus high), were detected in these two case studies. The findings of these case studies indicated that among our participants, the drivers' HR had a higher fraction of abnormal patterns during harsh brakes, accelerating and curved driving. Additionally, free-flow driving with close to zero accelerations on the highway was accompanied by more fraction of normal HR as well as a lower gaze entropy pattern. With the proposed methodology we can better understand variations in driver's psychophysiological states within different driving scenarios. The findings of this work, has the potential to guide future autonomous vehicles to take actions that are fit to each specific driver.
自然驾驶数据 (NDD) 可以帮助理解驾驶员对每个驾驶场景的反应，并为驾驶行为提供个性化的背景信息。然而，NDD 需要大量的人工劳动来标注驾驶员的状态和行为模式。对 NDD 的无监督分析可以用于自动从驾驶员和车辆数据中检测不同的模式。在本文中，我们提出了一种方法来理解不同驾驶模式下驾驶员生理反应的变化。我们的方法首先使用贝叶斯变点检测模型对驾驶场景进行分解。然后，我们将潜在狄利克雷分配方法应用于驾驶员状态和行为数据，以检测模式。我们提出了两个案例研究，在这些案例研究中，车辆配备了采集外部、内部和驾驶员行为数据的设备。在这两个案例研究中，检测到了四种驾驶行为模式（即急刹车、正常刹车、弯道驾驶和高速公路驾驶）以及两种驾驶员心率（HR）模式（即正常与异常高 HR）和注视熵模式（即低与高）。这些案例研究的结果表明，在我们的参与者中，驾驶员的 HR 在急刹车、加速和弯道驾驶时异常模式的比例更高。此外，高速公路上接近零加速度的自由行驶伴随着正常 HR 比例更高和注视熵模式更低。通过提出的方法，我们可以更好地理解不同驾驶场景下驾驶员心理生理状态的变化。这项工作的结果有可能指导未来的自动驾驶汽车采取适合每个特定驾驶员的行动。
