驾驶特性的识别评估及其在智能汽车上的应用综述

郭烈; 马跃; 岳明; 秦增科

doi:10.19818/j.cnki.1671-1637.2021.02.002

驾驶特性的识别评估及其在智能汽车上的应用综述

doi: 10.19818/j.cnki.1671-1637.2021.02.002

大连理工大学汽车工程学院, 辽宁大连 116024

基金项目:

国家自然科学基金项目 51975089

国家自然科学基金项目 61873047

详细信息

作者简介:
郭烈(1978-)，男，江西新余人，大连理工大学副教授，工学博士，从事智能车辆、人机共驾与协同控制研究

中图分类号: U471
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出版历程
- 收稿日期: 2020-10-04
- 刊出日期: 2021-04-01

Overview of recognition and evaluation of driving characteristics and their applications in intelligent vehicles

School of Automotive Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China

Funds:

National Natural Science Foundation of China 51975089

National Natural Science Foundation of China 61873047

More Information

Author Bio:
GUO Lie(1978-), male, associate professor, PhD, guo_lie@dlut.edu.cn

摘要

摘要: 研究了驾驶特性的识别方法、驾驶人接管能力评估的进展、驾驶特性在智能汽车领域中的应用；将驾驶人状态监测划分为驾驶人疲劳监测、分心监测和不良驾驶行为监测，总结了驾驶人状态监测研究的目标、方法、精确度、判断标准以及优缺点；对比了驾驶人疲劳监测中不同检测信号之间的差异；评析了基于模糊识别和隐马尔可夫模型的驾驶人意图识别与预测方法；梳理了驾驶风格分类与辨识的主要步骤、典型辨识方法的特点；分析了驾驶人接管能力的影响因素与评判标准；阐述了驾驶特性用于开发用户接受度高和人机交互性能好的辅助驾驶系统的主要方式；概括了在人机共驾协同控制中考虑驾驶特性的途径。研究结果表明：基于多种传感器信号融合的驾驶人状态监测可有效避免基于单一传感器信号的弊端，提高了检测精度，减少了误警报；将传统预测模型与混合智能学习相融合的方法能够为驾驶意图在线识别与预测提供解决方案；应该重点研究复杂工况下的驾驶特性辨识；驾驶人接管能力的研究有待理论化和系统化；未来的发展趋势是开发基于驾驶特性的集成辅助驾驶技术、实现多种典型路况下驾驶人与辅助驾驶系统进行意图和控制策略的交互；将个性化驾驶人的驾驶特性融入共驾系数的设计中，从而提高人机共驾系统的个性化、智能化水平和环境适应性能。
- 汽车工程 /
- 智能汽车 /
- 驾驶特性 /
- 辅助驾驶 /
- 人机共驾
Abstract: The methods for the recognition of driving characteristics, the research progress on driver takeover ability, and the application of driving characteristics to the field of intelligent vehicles were studied. The driver condition monitoring was divided into driver fatigue, distraction, and bad driving behavior monitoring. The research targets, methods, accuracy, judgment standards, and advantages and disadvantages of driver condition monitoring were summarized. The differences in various detection signals in the driver fatigue monitoring method were compared and analyzed. The methods for driver intention identification and prediction based on the fuzzy recognition and hidden Markov models were discussed and evaluated. The main steps and features of typical identification methods for driving style classification and identification were summarized. The influencing factors and evaluation criteria for driver takeover ability were analyzed. The major ways that driving characteristics were used to develop assistant driving systems with high user acceptance and excellent human-machine interaction performance were expounded. The approach considering the driving characteristics in human-machine co-driving cooperative control was summarized. Analysis result shows that driver condition monitoring methods based on the multi-sensor signal fusion can effectively avoid the disadvantages of single sensor-based methods, and increase the detection accuracy, and decrease the false alarms. Combining traditional prediction models with hybrid intelligent learning is the main solution for the online recognition and prediction of driving intentions. The identification of driving characteristics under complex conditions is the primary research focus. The research on driver takeover ability needs to be theoretical and systematic. Developing an integrated assistant driving technology based on driving characteristics and realizing the interaction of intention and control strategy between the driver and the assistant driving system under typical road conditions is a future research trend. Considering the driving characteristics of personalized drivers in the design of co-driving coefficients helps to improve the personalization, intelligence level, and environmental adaptability of human-machine co-driving systems. 4 tabs, 5 figs, 82 refs.
- automotive engineering /
- intelligent vehicle /
- driving characteristic /
- assistant driving /
- human-machine co-driving

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图 1 驾驶人意图识别与预测方法

Figure 1. Methods for driver intention recognition and prediction

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图 2 驾驶风格分类与辨识

Figure 2. Classification and identification for driving styles

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图 3 基于驾驶特性的驾驶辅助系统开发

Figure 3. Development of driving assistance systems based on driving characteristics

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图 4 人机共驾协同控制框架

Figure 4. Human-machine co-driving cooperative control framework

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图 5 基于人机共驾的车道保持控制系统

Figure 5. Lane keeping system based on human-machine co-driving

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表 1 驾驶人疲劳状态监测文献对比

Table 1. Comparison of literatures about driver fatigue state monitoring

文献	目标	精确度	算法及方法	判断标准	优点	待改进之处
[9]	疲劳等级1~5	99.64%	AdaBoost算法、SIFT特征点匹配算法	面部SVM，BP神经网络	结合面部与手部，克服光照、背景、角度及个体差异影响，鲁棒性强	无实车试验，特征点的提取有待优化
[10]	疲劳等级1~6	95.1%	Viola-Jones算法、ASM算法、HOG算法	SVM	适应光照变化，鲁棒性强，准确率高	难适应实际复杂的驾驶遮挡行为
[11]	嗜睡监测	车道保持PPV 76.9%敏感性88.7%	马氏距离	监测EEG频谱与警戒模型的偏差	功率消耗低、体积小、实时无线的脑电计算机接口	需建立警戒模型、对设备要求高
[12]	嗜睡监测		FastICA算法	功率谱密度	采集多通道信号，消除眼电、肌电和工频干扰	缺少判断疲劳的量化指数标准
[13]	嗜睡监测	睡意发作检测器阳性预测率96%	积分脉冲调频	心率变异性	设计睡意发作监测器和睡眠剥夺监测器，针对性强	基于小规模人群、横断设计
[15]	疲劳估计		FFT、小波变换	基于方向盘角度的混沌理论	将混沌理论应用于驾驶人疲劳状态估计	小波分析的实时性和准确性有待提高，无疲劳估计指数标准
[16]	酒后嗜睡评估		方差分割图	横向位置和速度的标准偏差	与以往的研究作比较，承接性好	试验成本高，应用性有待提高

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表 2 疲劳监测中不同检测信号的特点

Table 2. Characteristics of different detection signals in fatigue detection

信息	检测成本	鲁棒性	优点	缺点
机器视觉	低	高	无需穿戴	易受光线和遮挡影响
生理心理	高	高	精度高^[11]	需穿戴设备
车辆	低	低	易于获取	精度低^[16]

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表 3 意图识别典型方法特点

Table 3. Characteristics of typical methods for intention recognition

方法	模糊识别	HMM
算法原理	根据以往经验设计模糊规则，根据设计者期望修改模型性能	描述随机过程的统计特性，并通过外部观察序列识别固有的不可见状态
模型精确度	高^[29-30]	极高^[33]
实时性	一般	好^[39]
缺点	模糊规则固定，使仿真结果存在不可避免的偏差	不适合长期预测系统，当前状态的序列需要人为假设
应用	适用于参数范围难以确定的意图识别	适用于转向、换道等强时间序列的意图识别

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表 4 驾驶风格分类辨识方法特点

Table 4. Features of classification and identification methods of driving styles

方法	K-means	BP神经网络	SVM
算法原理	根据数据集中样本的相似性将样本分为K簇	通过输入数据不断调整模型网络的权值，实现输入与输出之间的映射，学习识别驾驶行为	寻找满足约束的最优分类超平面，使不同类别样本之间间隔最大化。
模型精确度	与K值有关	极高^{[42, 45]}	高^{[42, 49]}
缺点	最佳聚类数不易确定，硬聚类使得每个驾驶人只能有一种风格	特征参数的质量对精确度的影响大，基于结果改进模型参数，训练时间长	存储空间大，常与贝叶斯过滤器组合改善驾驶人行为分类效果
应用	连续型的数据	适合易于访问获取特征参数的识别	高维、小样本、非线性的识别

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