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首页> 外文期刊>Human-Machine Systems, IEEE Transactions on >A Personalizable Driver Steering Model Capable of Predicting Driver Behaviors in Vehicle Collision Avoidance Maneuvers
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A Personalizable Driver Steering Model Capable of Predicting Driver Behaviors in Vehicle Collision Avoidance Maneuvers

机译：个性化的驾驶员转向模型，能够预测驾驶员在避撞行为中的行为

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摘要

In recent years, significant emphases and efforts have been placed on developing and implementing advanced driver assistance systems (ADAS). These systems need to work with human drivers to increase vehicle occupant safety, control, and performance in both ordinary and emergency driving situations. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined driver model that is able to not only identify different individual driver behaviors, but also predict a driver's behavior in rare vehicle maneuvers such as collision avoidance (CA) based on his/her daily driving data. The driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. It has been shown that the proposed driver model can replicate each driver's steering wheel angle signal for a variety of highway and in-city maneuvers. The utility of the proposed driver model is its ability to predict a driver's steering wheel angle signal for a CA maneuver from only daily nonemergency driving data. The driver model is then validated by comparing two different drivers' model parameter sets to the group average to show that each driver has a unique set of parameters. Finally, the driver model is validated by showing that its daily driving parameters differ from its predicted CA parameters.
机译：近年来，在开发和实施高级驾驶员辅助系统（ADAS）方面已投入了大量精力和精力。这些系统需要与驾驶员合作，以提高普通和紧急驾驶情况下车辆乘员的安全性，控制能力和性能。为了帮助人类驾驶员和ADAS之间进行这种合作，驾驶员模型对于复制和预测人类驾驶行为以及区分不同驾驶员是必要的。本文提出了一种组合驾驶员模型，该模型不仅能够识别不同的驾驶员行为，而且还可以基于驾驶员/驾驶员的日常驾驶数据来预测驾驶员在罕见车辆操纵中的行为，例如避撞（CA）。驾驶员模型由补偿传递函数和预期组件组成，并与单个驾驶员所需路径的设计集成在一起。已经表明，提出的驾驶员模型可以针对各种高速公路和城市机动来复制每个驾驶员的方向盘角度信号。所提出的驾驶员模型的实用性是它能够仅从每日非紧急驾驶数据预测CA操纵的驾驶员方向盘角度信号的能力。然后，通过将两个不同的驾驶员模型参数集与组平均值进行比较来验证驾驶员模型，以表明每个驾驶员都具有唯一的一组参数。最后，通过显示其日常驾驶参数与预测的CA参数不同来验证驾驶员模型。

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