個人化リスクマップによる運転支援の警告エラー削減
Reducing Warning Errors in Driver Support with Personalized Risk Maps
運転者の行動から個人化リスク要因を推定し、個人化リスクマップで警告信号を適応させることで、誤警告と警告漏れを削減する手法を提案した。
著者: Tim Puphal, Ryohei Hirano, Takayuki Kawabuchi, Akihito Kimata, Julian Eggert
分類: cs.RO, cs.LG
原文アブストラクト
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.
