Driver-automation mutual adaptation: modeling, design, and evaluation of haptic interface for cooperative driving tasks

我们的研究重点是驾驶员-自动化相互适应以实现触觉共享控制。使用深度学习网络开发了一个驾驶员模型，并在有9名参与者的驾驶模拟器实验中收集的数据上进行了训练，并证明了该模型在预测单个驾驶员的目标轨迹方面是有效的。该驱动程序模型用于共享控件的进一步设计.在间接共享控制框架下，提出了一种车道保持的自适应控制分配算法。该算法采用基于信任的数据驱动共享控制策略，融合了驾驶员和自动化的控制输入。自动控制代理使用Koopman模型预测控制计算。自适应控制权限分配使用混合人机信任模型来实现，该模型基于从人机自动化交互和驾驶员输入意图推断的信任机制。在一个有五个参与者的交互式仿真环境中，该算法被证明是有效的和有益的.针对自动驾驶汽车的驾驶员超控问题，提出了一种基于共享控制策略的转向辅助系统。当车辆启动故障安全操纵时，系统考虑驾驶员对超驰的潜在需求。采用基于驾驶员可控性的共享控制策略，在车辆脱离危险时实现驾驶权的平稳转移。该系统的有效性证明在多车道高速公路的情况下。

Our research focuses on driver-automation mutual adaptation for haptic shared control.In FY 2022, our research achievements are summarized as follows:1. A driver model was developed using a deep learning network and was trained on data collected from a driving simulator experiment with nine participants, and is demonstrated to be effective in predicting individual driver's target trajectory. The driver model is applied for further design of shared control.2. An adaptive control allocation algorithm was developed for lane keeping under an indirect shared control framework. The algorithm uses a trust-based data-driven shared control strategy and blends control inputs of drivers and automation. The automated control agent is computed using Koopman model predictive control. Adaptive control authority allocation is achieved using a hybrid human-to-machine trust model based on a trust mechanism inferred from human-automation interaction and driver input intention. The proposed algorithm is demonstrated to be effective and beneficial in an interactive simulation environment with five participants.3. A steering assistance system involving a shared control strategy was developed for driver override in automated vehicles. The system considers the potential driver demand for override when the vehicle initiates a fail-safe maneuver. A shared control strategy based on driver controllability is adopted to smoothly transfer driving authority when the vehicle is out of danger. The effectiveness of the proposed system is demonstrated in multi-lane highway scenarios.