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.

翻译：本文针对基于人类行为的驾驶员支持问题展开研究。当前，驾驶员支持系统已在多种驾驶情境中协助用户安全操作。然而，这些系统尚未充分利用通过感知人类驾驶员所获得的丰富信息。为此，本文提出一种基于人类行为的风险模型，该模型利用驾驶员信息以提升驾驶支持效能。与现有技术相比，我们提出的风险模型融合了以下两方面要素：a) 基于驾驶员错误（如忽视其他车辆等注意失误）的当前驾驶员感知状态；b) 驾驶员个性化特征（如防御型或自信型驾驶倾向）。通过对多种交互式驾驶场景的广泛仿真实验，我们证明相较于未使用人类驾驶员信息的基准风险模型，我们提出的新型人类行为风险模型能够实现更早的预警时机并降低预警错误率。