This paper presents a shared control driver assistance system based on the driving intention identification and situation assessment to avoid obstacles. A constrained linear-time-varying model predictive controller is designed to follow the obstacle-avoidance path, which is obtained by the artificial potential method in real time. A human driver's driving intention and the desired maneuver are recognized by the inductive multilabel classification with an unlabeled data approach that is trained based on the lateral offset and lateral velocity to the road center line. In addition, the situation assessment of the collision risk is represented by the time to collision and the performance evaluation is designed according to lateral deviation. All of them are employed for the design of the shared control fuzzy controller. The cooperative coefficient, denoting the control authority between the controller and a human driver, is determined by three fuzzy controllers in different conditions, which are the consistent, the advanced inconsistent, and the lagged inconsistent fuzzy controller, respectively. More importantly, there are two scenarios studies provided to verify the proposed system. The results prove that the shared control driver assistance system can successfully help drivers to avoid obstacles and obtains great vehicle stability performance in different scenarios.

中文翻译：

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基于驾驶意图和态势评估的共享控制驾驶辅助系统


本文提出了一种基于驾驶意图识别和态势评估的避障共享控制驾驶辅助系统。设计了约束线性时变模型预测控制器来跟踪通过人工势法实时获得的避障路径。人类驾驶员的驾驶意图和所需的操纵是通过归纳多标签分类和未标记数据方法来识别的，该方法是根据道路中心线的横向偏移和横向速度进行训练的。此外，碰撞风险的态势评估以碰撞时间来表示，性能评估则根据横向偏差进行设计。它们都被用于共享控制模糊控制器的设计。协同系数表示控制器与人类驾驶员之间的控制权限，由不同条件下的三种模糊控制器决定，分别是一致模糊控制器、先进不一致模糊控制器和滞后不一致模糊控制器。更重要的是，提供了两个场景研究来验证所提出的系统。结果证明，共享控制驾驶辅助系统能够成功帮助驾驶员避障，并在不同场景下获得良好的车辆稳定性表现。