Towards Multimodal Driver's Stress Detection
Towards Multimodal Driver's Stress Detection
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DOI：
10.1007/978-1-4419-9607-7_1
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发表时间：
2012-01-01
期刊：
影响因子：
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通讯作者：
Hansen, John H. L.
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文献类型：
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作者：
Boril, Hynek;Boyraz, Pinar;Hansen, John H. L.
非驾驶相关的认知负荷和情绪状态的变化可能会影响驾驶员控制车辆的能力并导致驾驶错误。驾驶员压力检测的可用性将有利于主动安全系统和其他智能车载接口的设计。在本章中，我们提出了在城市驾驶场景中进行多模式驾驶员压力（分心）检测的初步步骤，涉及多任务处理、对话系统对话和中等水平的认知任务。目标是利用驾驶员语音和 CAN 总线信号获得连续的操作模式检测，并直接应用于智能人车界面，以适应驾驶员的实际状态。首先，分析了各种驾驶场景对语音产生特征的影响，然后设计了基于语音的压力检测器。在独立于驾驶员/操纵的开放测试集任务中，系统的中性/压力分类准确率达到 88.2%。其次，在依赖于驾驶员/操纵的封闭测试集任务中引入并评估了利用 CAN-Bus 信号的分心检测，在车道保持段和弯道协商段中分别达到 98% 和 84% 的分心检测准确度。自主分类器的性能表明，未来语音和 CAN-Bus 信号域的融合将产生一个整体稳健的压力评估框架。
Non-driving-related cognitive load and variations of emotional state may impact the drivers' capability to control a vehicle and introduce driving errors. The availability of stress detection in drivers would benefit the design of active safety systems and other intelligent in-vehicle interfaces. In this chapter, we propose initial steps towards multimodal driver stress (distraction) detection in urban driving scenarios involving multitasking, dialog system conversation, and medium-level cognitive tasks. The goal is to obtain a continuous operation-mode detection employing driver's speech and CAN-Bus signals, with a direct application for an intelligent human vehicle interface which will adapt to the actual state of the driver. First, the impact of various driving scenarios on speech production features is analyzed, followed by a design of a speech-based stress detector. In the driver-/maneuver-independent open test set task, the system reaches 88.2% accuracy in neutral/stress classification. Second, distraction detection exploiting CAN-Bus signals is introduced and evaluated in a driver-/maneuver-dependent closed test set task, reaching 98% and 84% distraction detection accuracy in lane keeping segments and curve negotiation segments, respectively. Performance of the autonomous classifiers suggests that future fusion of speech and CAN-Bus signal domains will yield an overall robust stress assessment framework.