当前位置: X-MOL 学术IEEE ASME Trans. Mechatron. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
MPC-Based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving
IEEE/ASME Transactions on Mechatronics ( IF 6.4 ) Pub Date : 2021-03-04 , DOI: 10.1109/tmech.2021.3063902
Andrea Michelle Lazcano , Tenghao Niu , Xabier Carrera Akutain , David Cole , Barys Shyrokau

Advanced driver assistance systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver–vehicle interaction often leads to reduced user acceptance. In this article, an optimal torque control law is calculated online in the model predictive control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behavior, neuromuscular dynamics, and the vehicle-steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a linear-quadratic-gaussian, sensory organs, and neuromuscular dynamics, including muscle coactivation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial lane keeping assist system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver–vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving the path-tracking performance. Summarising, this article tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy.

中文翻译:

基于 MPC 的触觉共享转向系统:一种用于共生驾驶的驾驶员建模方法

高级驾驶辅助系统 (ADAS) 旨在提高安全性并减少脑力负担。然而,对闭环驾驶员-车辆交互的理解差距通常会导致用户接受度降低。在本文中,在模型预测控制 (MPC) 框架中在线计算最佳扭矩控制律,以保证在转向任务期间的连续引导。研究贡献在于将涵盖认知行为、神经肌肉动力学和车辆转向动力学的广泛预测模型集成到基于 MPC 的触觉控制器中以增强协作。驾驶员模型由基于线性二次高斯、感觉器官和神经肌肉动力学(包括肌肉共激活和反射动作)的预览认知策略组成。而且,自适应成本函数算法可以实现控制权限的动态分配。实验是在丰田汽车欧洲公司的固定基础驾驶模拟器中进行的,涉及 19 名参与者,以针对作为行业基准的商业车道保持辅助系统评估具有两种不同成本函数的拟议控制器。结果表明,相对于最先进的商业系统,所提出的控制器在主观和客观上促进了共生驾驶并减少了驾驶员与车辆的冲突,同时仍提高了路径跟踪性能。总而言之,本文解决了融合人类和 ADAS 控制的需求,证明了所提出策略的有效性。实验是在丰田汽车欧洲公司的固定基础驾驶模拟器中进行的,涉及 19 名参与者,以针对作为行业基准的商业车道保持辅助系统评估具有两种不同成本函数的拟议控制器。结果表明,相对于最先进的商业系统,所提出的控制器在主观和客观上促进了共生驾驶并减少了驾驶员与车辆的冲突,同时仍提高了路径跟踪性能。总而言之,本文解决了融合人类和 ADAS 控制的需求,证明了所提出策略的有效性。实验是在丰田汽车欧洲公司的固定基础驾驶模拟器中进行的,涉及 19 名参与者,以针对作为行业基准的商业车道保持辅助系统评估具有两种不同成本函数的拟议控制器。结果表明,相对于最先进的商业系统,所提出的控制器在主观和客观上促进了共生驾驶并减少了驾驶员与车辆的冲突,同时仍提高了路径跟踪性能。总而言之,本文解决了融合人类和 ADAS 控制的需求,证明了所提出策略的有效性。结果表明,相对于最先进的商业系统,所提出的控制器在主观和客观上促进了共生驾驶并减少了驾驶员与车辆的冲突,同时仍提高了路径跟踪性能。总而言之,本文解决了融合人类和 ADAS 控制的需求,证明了所提出策略的有效性。结果表明,相对于最先进的商业系统,所提出的控制器在主观和客观上促进了共生驾驶并减少了驾驶员与车辆的冲突,同时仍提高了路径跟踪性能。总而言之,本文解决了融合人类和 ADAS 控制的需求,证明了所提出策略的有效性。
更新日期:2021-03-04
down
wechat
bug