基于驾驶员状态的预警模型，提升交互驾驶场景中的安全支持。
Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios
- 融合驾驶员感知错误与个性特征评估风险。
- 预警时间提前，误报率显著降低。
- 适合智能座舱与自适应辅助驾驶系统研发者。
本文针对人类驱动支持问题，提出一种基于驾驶员的新型风险模型。当前驾驶辅助系统虽能保障多数场景下的安全操作，但未能充分挖掘驾驶员状态传感信息。为此，本研究构建的人类风险模型结合了两方面信息：一是基于驾驶员错误（如忽略其他车辆）的实时感知能力；二是驾驶员个性特征（如防御性或自信）。在多种交互式驾驶场景的大量仿真测试中，该模型相较不使用驾驶员信息的基准模型，实现了更早的预警时间与更低的误报率，显著提升了驾驶支持系统的响应性能。
原文摘要 · Abstract (English)
This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model not using human driver information.
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