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

驾驶员-自动化相互适应：协作驾驶任务的触觉界面的建模、设计和评估

• 批准号: 21K17781
• 负责人: 王 正
• 金额: $ 2.25万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K17781/
• 关键词: HMI; Automated driving; Driver behavior modeling; Human-centered design; Machine learning; Intelligent vehicles; Shared control; Haptic interface; Mutual adaptation

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.

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是使用一个混合的人机信任模型的基础上推断的信任机制，从人与自动化的互动和驾驶员的输入意图。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。

项目成果

A Fail-safe System involving Shared Control Strategy for Driver Override

涉及驱动程序覆盖共享控制策略的故障安全系统

• DOI: 10.1016/j.ifacol.2022.10.558
• 发表时间: 2022
• 期刊: IFAC-PapersOnLine
• 作者: Xue Wei;Wang Zheng;Yang Bo;Zheng Rencheng;Nakano Kimihiko
• 通讯作者: Nakano Kimihiko

Modeling and analysis of driver behaviour under shared control through weighted visual and haptic guidance

• DOI: 10.1049/itr2.12163
• 发表时间: 2022-01-13
• 期刊: IET INTELLIGENT TRANSPORT SYSTEMS
• 影响因子: 2.7
• 作者: Wang，Zheng;Zheng，Rencheng;Nakano，Kimihiko
• 通讯作者: Nakano，Kimihiko

Trust-based Data-driven Shared Control for Lane-keeping

基于信任的数据驱动的车道保持共享控制

• 发表时间: 2022
• 作者: Nacpil Edric John Cruz;Wang Zheng;Nakano Kimihiko;Wei Xue;Shuo Cheng;Shuo Cheng;Muhua Guan;Daihong Wan
• 通讯作者: Daihong Wan

Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study

• DOI: 10.1109/ojits.2022.3222442
• 发表时间: 2022
• 期刊: IEEE Open Journal of Intelligent Transportation Systems
• 影响因子: 2.6
• 作者: Zheng Wang;Muhua Guan;Jin Lan;Bo Yang;T. Kaizuka;Junichi Taki;Kimihiko Nakano

Indicator of Safety and Comfort Performance: Quantifying Different Driving Styles for Automated Vehicles

• DOI: 10.1109/itsc55140.2022.9922265
• 发表时间: 2022-10
• 期刊: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
• 作者: Shuo Cheng;Zheng Wang;Bo Yang;Kimihiko Nakano