This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

中文翻译：

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基于人类驾驶数据的自主车辆主动变道控制设计与实现

本文介绍了考虑人为因素的自动驾驶汽车主动变道控制算法的设计、实现和评估。考虑到驾驶员的接受度和周围车辆的安全性，需要执行车道变换。因此，需要基于对人类驾驶行为的分析来设计自动驾驶系统。在本文中，使用真实世界的驾驶测试数据来研究手动驾驶特性。在变道情况下，主要研究与周围车辆的相互作用。并通过运动学分析开发了安全指标。已经开发了基于安全指数的车道变换决策和控制算法。为了提高安全性，考虑到传感器噪声和模型的不确定性，已经对自我车辆和周围车辆进行了随机预测。决定所需的驾驶模式以应对高速公路上的所有车道变换。为了获得所需的参考和约束，设计车道变换的运动规划时考虑了基于随机预测的安全指标。已采用具有约束的随机模型预测控制来确定车辆控制输入：转向角和纵向加速度。所提出的主动变道算法已在自动驾驶汽车上成功实施，并通过实际驾驶测试进行了评估。我们的自动驾驶测试车已经证明了在高速公路上高速行驶时安全舒适的车道变换。