虚拟随机车路场下驾驶人驾驶能力机理分析

doi:10.3901/JME.2020.16.166

机械工程学报 ›› 2020, Vol. 56 ›› Issue (16): 166-180.doi: 10.3901/JME.2020.16.166

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虚拟随机车路场下驾驶人驾驶能力机理分析

孙博华¹, 邓伟文^1,2, 吴坚¹, 李雅欣¹

1. 吉林大学汽车仿真与控制国家重点实验室长春 130022;
2. 北京航空航天大学交通科学与工程学院北京 430071

收稿日期:2019-12-01 修回日期:2020-03-18 出版日期:2020-08-20 发布日期:2020-10-19
通讯作者: 邓伟文(通信作者),男,1963年出生,博士,教授,博士研究生导师。主要研究方向为基于人性化和个性化的汽车智能辅助驾驶及自主操纵。E-mail:kwdeng@188.com
作者简介:孙博华,男,1988年出生,博士研究生。主要研究方向为基于人性化和个性化的汽车智能辅助驾驶及自主操纵。E-mail:sunbh14@mails.jlu.edu.cn;吴坚,男,1977年出生,博士,教授,博士研究生导师。主要研究方向为汽车先进电控、整体优化和集成控制。E-mail:wujian@jlu.edu.cn;李雅欣,女,1992年出生,博士研究生。主要研究方向为面向汽车虚拟测试的传感器建模和越野条件下自动驾驶关键问题。E-mail:liyaxin0930@foxmail.com
基金资助:
国家重点研发计划（2016YFB0100904）和国家自然科学基金（U1564211，51775235）资助项目。

Mechanism Analysis of Driving Capability in the Virtual Random Vehicle-road Field

SUN Bohua¹, DENG Weiwen^1,2, WU Jian¹, LI Yaxin¹

1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022;
2. School of Transportation Science and Engineering, Beihang University, Beijing 430071

Received:2019-12-01 Revised:2020-03-18 Online:2020-08-20 Published:2020-10-19

摘要/Abstract

摘要： 为解决智能汽车人机协同共驾驾驶权仲裁的普适性和规模可控性问题，提出虚拟随机车路场下的驾驶人驾驶能力机理分析及评估体系。作为适应于驾驶人驾驶能力机理分析的评估环境，虚拟随机车路场模型通过耦合车辆运动模型及车路可行驶区域空间拓扑结构的时空状态，得到可揭示微观驾驶场景下人-车-路耦合机理及车路协同规律的驾驶场景。驾驶能力机理分析体系在严格定义驾驶能力的基础上，建立满足高阶非线性驾驶能力属性的离线辨识模型。模型中关键参数经解耦和降维组成的样本集，为表征驾驶能力内在属性的关键依据。采用主客观相结合方式分类驾驶能力，采用基于混合高斯隐马尔科夫过程实现驾驶能力的实时辨识。结果表明，所提出的虚拟随机车路场模型可以客观地反映交通流的波粒二象性，通过虚拟随机车路场下的驾驶能力机理分析，可以得到准确且可靠的驾驶能力评价结果。虚拟随机车路场下的驾驶人驾驶能力机理分析体系，优化了驾驶权仲裁机制的合理性，提高了人机协同共驾系统的安全性和驾驶人可接受性。

关键词: 车辆工程, 随机车路场, 驾驶能力, 机器学习, 系统辨识

Abstract: To solve the universality and scale controllability issue of the driving authority arbitration in shared control, an analysis and evaluation framework of the driving capability mechanism in the virtual random vehicle-road field(RVRF) is proposed. As the assessment environment for the mechanism analysis of the driving capability, driving scenarios those can reveal the human-vehicle-road coupling mechanism and vehicle-road cooperative rules by coupling the space topology of drivable area with the vehicle motion patterns are obtained in the RVRF. Basing on the strict definition of driving capability, the off-line identification model satisfies the high-order nonlinear attributes of driving capability is established in the RVRF evaluation framework. The sample set consisting of the decoupled and dimensionless key parameters in the model is the key basis to represent the intrinsic attribute of driving capability. The classification is done basing on the combination of subjective and objective and driving capability is identified in real time basing on Gaussian multi-dimension hidden markov model(GM-HMM). Results show that the proposed virtual RVRF can objectively reflect the wave-particle duality of traffic flow and the proposed evaluation method for driving capability in the virtual RVRF can achieve accurate and reliable evaluation results. The mechanism analysis of driving capability in the virtual RVRF optimizes the rationality of driving authority arbitration mechanism, and improves the safety and driver acceptability for the shared control system.

Key words: vehicle engineering, random vehicle-road field, driving capability, machine learning, system identification

中图分类号:

U461

孙博华, 邓伟文, 吴坚, 李雅欣. 虚拟随机车路场下驾驶人驾驶能力机理分析[J]. 机械工程学报, 2020, 56(16): 166-180.

SUN Bohua, DENG Weiwen, WU Jian, LI Yaxin. Mechanism Analysis of Driving Capability in the Virtual Random Vehicle-road Field[J]. Journal of Mechanical Engineering, 2020, 56(16): 166-180.

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虚拟随机车路场下驾驶人驾驶能力机理分析

Mechanism Analysis of Driving Capability in the Virtual Random Vehicle-road Field

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