On the Use of Stochastic Driver Behavior Model in Lane Departure Warning
On the Use of Stochastic Driver Behavior Model in Lane Departure Warning
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DOI：
10.1109/tits.2010.2072502
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发表时间：
2011-03-01
影响因子：
8.5
通讯作者：
Wakita, Toshihiro
中科院分区：
文献类型：
--
作者：
Angkititrakul, Pongtep;Terashima, Ryuta;Wakita, Toshihiro
在本文中，我们提出了一个新的框架来区分初始机动的车道交叉事件从驾驶员校正事件，这是错误的警告车道偏离预测系统（LDPSs）的主要原因。所提出的算法验证了驱动信号轨迹的开始情节，即，通过采用表示车道交叉和驾驶员校正事件的分段横向斜坡的方向序列（DSPLS）的驾驶员行为模型，来确定其是否将引起车道交叉事件。该框架只利用常见的驾驶信号，并允许驾驶员行为模型的自适应方案，以更好地代表个人驾驶特征。实验评估表明，建议DSPLS框架具有低至17%的等错误率的检测错误。此外，该算法降低了原车道偏离预测系统的误报率，与正确的预测较小的折衷。
In this paper, we propose a new framework for discriminating the initial maneuver of a lane-crossing event from a driver correction event, which is the primary reason for false warnings of lane departure prediction systems (LDPSs). The proposed algorithm validates the beginning episode of the trajectory of driving signals, i.e., whether it will cause a lane-crossing event, by employing driver behavior models of the directional sequence of piecewise lateral slopes (DSPLS) representing lane-crossing and driver correction events. The framework utilizes only common driving signals and allows the adaptation scheme of driver behavior models to better represent individual driving characteristics. The experimental evaluation shows that the proposed DSPLS framework has a detection error with as low as a 17% equal error rate. Furthermore, the proposed algorithm reduces the false-warning rate of the original lane departure prediction system with less tradeoff for the correct prediction.