基于驾驶行为个性化风险地图，减少预警误报和漏报。
Reducing Warning Errors in Driver Support with Personalized Risk Maps
- 根据驾驶员行为构建个性化风险因子
- 在纵向跟车和交叉口场景中降低误报与漏报
- 适合需要精准预警的智能辅助驾驶系统
本文研究以人为中心的驾驶支持问题。当前先进的个性化方法可估计车辆控制系统或驾驶员模型的参数，但极少有方法将个性化模型用于通用风险预警的评估。为此，我们提出一种预警系统：基于驾驶员行为估算个性化风险因子，并据此自适应调整预警信号，生成个性化风险地图。实验表明，在纵向跟车和交叉口场景中，新系统相比不考虑个性化的基线方法，显著减少了误报和漏报错误，验证了个性化在降低预警误差方面的潜力。
原文摘要 · Abstract (English)
We consider the problem of human-focused driver support. State-of-the-art personalization concepts allow to estimate parameters for vehicle control systems or driver models. However, there are currently few approaches proposed that use personalized models and evaluate the effectiveness in the form of general risk warning. In this paper, we therefore propose a warning system that estimates a personalized risk factor for the given driver based on the driver's behavior. The system afterwards is able to adapt the warning signal with personalized Risk Maps. In experiments, we show examples for longitudinal following and intersection scenarios in which the novel warning system can effectively reduce false negative errors and false positive errors compared to a baseline approach which does not use personalized driver considerations. This underlines the potential of personalization for reducing warning errors in risk warning and driver support.
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