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.

翻译：本文探讨以驾驶者为中心的驾驶辅助问题。当前先进的个性化理念能够为车辆控制系统或驾驶员模型估计参数。然而，目前鲜有研究提出利用个性化模型并以通用风险预警形式评估其有效性。为此，本文提出一种预警系统，该系统能根据驾驶员行为估算特定驾驶员的个性化风险系数，进而通过个性化风险地图自适应调整预警信号。实验部分展示了纵向跟车与交叉路口场景的案例，结果表明：相较于未考虑驾驶员个性化的基线方法，新型预警系统能有效降低漏报误差与误报误差。这印证了个性化技术在降低风险预警与驾驶辅助系统预警误差方面的潜力。