驾驶特性的识别评估及其在智能汽车上的应用综述

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。 

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

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.