考虑驾驶员感知的自主横向纠偏回归模型

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI:10.1109/CIS.2017.00096

Liang Qiao, H. Bao, Zuxing Xuan, Qing Yang

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引用次数: 0

摘要

由于自动驾驶车辆根据速度选择线性预览点，通常难以在期望路径和自我车辆之间进行像人一样的横向校正。本文提出了一种具有驾驶员感知的自动横向纠偏回归模型，用于描述预览点与速度之间的关系。在驾驶员感知模型(Driver Perception Model, DPM)中，车速与预览点距离呈对数关系。实验结果表明，与Stanley方法的横向纠偏相比，考虑驾驶员感知的自主横向纠偏回归模型可以获得更低的横向加速度，提高了车辆的舒适性;2)提高算法对参数整定的鲁棒性。

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Regression Model of Autonomous Lateral Rectification with Driver Perception

It is usually hard for autonomous vehicle to perform human-likely in lateral rectification between desired path and ego-vehicle, since those methods choose linear preview point according to speed. This paper presents a regression model of autonomous lateral rectification with driver perception to describe relationship between preview point and speed. It is shown in Driver Perception Model(DPM) that logarithmic relationship between speed and preview point distance. Experimental results show that compared with lateral rectification of Stanley method, regression model of autonomous lateral rectification with driver perception can 1) acquire lower lateral acceleration, which enhances comfort; 2) promote robustness of algorithm on parameter tuning.

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2017 13th International Conference on Computational Intelligence and Security (CIS)

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