All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

中文翻译：

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一种基于模糊逻辑的人性化驾驶员建模方法

所有人类驾驶员都可以通过他们对驾驶行为的习惯性选择来表征，这导致了广泛的观察到的驾驶模式和操纵。为解决此功能的自动驾驶汽车制定控制策略将增加公众对此类车辆的接受度。因此，本文提出了一种开发基于规则的模糊逻辑驱动程序模型的新方法，该模型可以模拟跟车情况下的不同驾驶风格。这些驾驶员模型使用收集的道路驾驶数据进行训练，以捕捉相应的人类驾驶员特征。所提出的方法由三个主要部分组成：收集道路驾驶数据、开发车辆模型和建立跟车司机模型。首先，使用仪表车辆在同一路线上连续三个月收集驾驶数据。提取这些旅程中的跟车场景，并相应地处理相关数据。之后，创建并评估了仪表车辆的代表性模型。最后，开发了一个使用人性化输入的模糊逻辑驱动模型，并使用记录的数据进行校准。使用收集的驾驶数据和基线 PID 驾驶员模型评估开发的驾驶员模型的性能。性能得到验证后，代表更激进和更具防御性的驾驶风格的模型按照相同的程序推导出来。然后在规范化的跟车场景中使用已建立的车辆模型进行交叉驾驶员分析，以进一步研究不同驾驶风格的影响。开发的驾驶员模型可以将驾驶风格引入驾驶循环实验中，并在实验室中复制道路实际驾驶排放测试。此外，由于所提出的方法对不完整的数据集具有很高的鲁棒性，因此它可以成为一种更具成本效益的选择，以促进自动驾驶的人性化和定制化车辆控制策略的开发。