重复驾驶条件下驾驶员记忆增长模型研究

驾驶员的记忆影响视觉搜索及路径规划等驾驶行为,进而影响道路通行效率与交通安全.为了描述重复驾驶条件下驾驶员记忆变化的特征,设计模拟驾驶实验,研究同一场景下重复驾驶对驾驶员记忆的累积刺激.通过场景记忆量表衡量驾驶员的记忆程度,分析了驾驶员记忆增长与重复驾驶次数的动态变化关系,分别采用单分子式、修正Weibull方程及Richards方程建立累积刺激作用下驾驶员记忆增长模型,并以误差平方和、均方根误差和调整 R2为评价指标对模型精度进行对比分析.结果表明,3种模型均能对驾驶员记忆增长特性进行描述,其中 Richards模型精度最高,其平均调整R2为0.9884.Richards模型揭示了记忆的同化与异化作用的本质,更适合建立重复驾驶条件下驾驶员对场景的记忆增长模型. 

Memory Growth Models of Drivers under Repeated Driving Environment

Memory affects driving behaviors of drivers such as visual search and route planning,and then influences efficiency and safety of road traffic.In order to describe characteristics of drivers′memory variation under repeated driv-ing situations,a driving simulation experiment is designed.Cumulative stimulus of repeated driving in a same scene is studied.A memory scale is adopted to measure memory degrees,and dynamic relationship between memory growth of drivers and the number of repeated driving is analyzed.Models of memory growth of drivers under cumulative stimulus are developed by using Mitscherlich,Modified Weibull,and Richards function,respectively.In addition,fitting effects of these models are compared by overall evaluation indices of adjusted determination coefficient,sum of the squared errors, and root mean square error.The results show that these three models can effectively describe the characteristics of driv-ers′memory growth.In conclusion,the model using Richards function has the best precision,of which the average adjus-ted R-square is 0.988 4,which reveals the essence of memory assimilation and dissimilation.It fits for being applied to study memory growth of drivers under repeated driving situations.