...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Transportation research >Driver profiling - Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation
【24h】

Driver profiling - Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation

机译：驱动程序概要分析-基于数据的驱动程序行为维度标识并影响驱动程序特性，以进行多代理程序流量模拟

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper focusses on the role of driver individuality in the field of cognitive driver behavior modeling for the prospective safety impact assessment of advanced driver assistance systems (ADAS) and automated driving functions. Virtual traffic simulation requires valid models for the environment, the vehicle and the driver. Especially modeling human driver behavior is a major challenge, which in recent years has already led to the development of various driver models for the purpose of virtual simulation. Modeling human behavior in traffic with a precise representation of human cognition, capability and individuality, are crucial demands, which require thorough investigation and understanding of the human driver. Current driver behavior models often leave aside the aspect of driver individuality and lack the consideration of differences in driving behavior between different drivers. To take into account all the aspects from complex human cognitive processes to individual differences in action implementation, the Stochastic Cognitive Model (SCM) was developed. The SCM is based on five subcomponents: gaze control, information acquisition, mental model, action manager and situation manager (=decision making process) and action implementation. The aim of the present study is to provide a basis for establishing a solid logic for the integration of driver individuality into the current structure of the SCM by creating a new submodule that takes into account several behavior affecting driver characteristics. This subcomponent controls the stochastic variance in several driver behavior parameters, such as velocity or comfort longitudinal acceleration. In a representative driving simulator study with 43 participants, driver behavior on the highway was investigated and thoroughly analyzed. Information about several relevant driver characteristics and personality traits of the participants was collected and a logical hierarchical model was set up to cluster several dependent and independent variables into four layers: independent manifest driver variables, such as age or gender (Level 1), latent driver personality factors, such as thrill seeking or anxiety (Level 2), driver behavior dimensions, such as dynamics and law conformity (Level 3), and various dependent driver behavior parameters, such as velocity, acceleration or speed limit violation (Level 4). Multiple linear regression analyses were run to find the individual driver characteristics and personality traits, by which most of the stochastic variance in the measured driver behavior parameters can be explained. Subsequently, a principal component analysis (PCA) was run to test, if the previously clustered driver behavior parameters were loading on the presumed behavioral dimensions on the third level of the model to identify significant components of driver behavior, such as dynamics or law conformity. Results of the present study show significant correlations between driver characteristics and driver behavior parameters.According to the results of the PCA, variability in driver behavior can be explained to a great extent by three largely independent components: (1) Speed and cruise control, (2) Dynamics and (3) Driver performance. With the consideration of driver individuality in driver behavior models for the agent-based traffic simulation, validity of the results from prospective safety impact assessment analyses of automated driving functions can be enhanced. Beyond that, the findings of the current study can be used as a solid basis for the development of adaptive functions in the field of vehicle automation, considering the different driving skills and preferences of drivers with different individual profiles. (C) 2019 Elsevier Ltd. All rights reserved.
机译：本文重点关注驾驶员个性在认知驾驶员行为建模领域的作用，用于高级驾驶员辅助系统（ADAS）和自动驾驶功能的前瞻性安全影响评估。虚拟交通仿真需要针对环境，车辆和驾驶员的有效模型。特别是对人类驾驶员行为进行建模是一个重大挑战，近年来，这已导致出于虚拟仿真目的开发各种驾驶员模型。至关重要的需求是对交通中的人类行为进行建模，以精确地表达人类的认知，能力和个性，这需要对驾驶员进行彻底的调查和理解。当前的驾驶员行为模型经常撇开驾驶员个性的方面，并且缺乏对不同驾驶员之间驾驶行为差异的考虑。考虑到从复杂的人类认知过程到动作执行中的个体差异的所有方面，开发了随机认知模型（SCM）。 SCM基于五个子组件：注视控制，信息获取，心理模型，动作管理器和情况管理器（=决策过程）以及动作实施。本研究的目的是通过创建一个新的子模块来建立将驾驶员个性整合到SCM当前结构中的坚实逻辑的基础，该模块考虑了几种影响驾驶员特征的行为。该子组件控制多个驾驶员行为参数（例如速度或舒适纵向加速度）中的随机方差。在一个有43名参与者的代表性驾驶模拟器研究中，对高速公路上的驾驶员行为进行了调查和彻底分析。收集有关参与者的几个相关驾驶员特征和性格特征的信息，并建立逻辑层次模型，将几个因变量和自变量分为四层：独立的清单驾驶员变量，例如年龄或性别（1级），潜在驾驶员个性因素，例如寻求刺激或焦虑（级别2），驾驶员行为维度（例如动力学和法律合规性）（级别3），以及各种相关的驾驶员行为参数，例如速度，加速度或违反速度限制（级别4）。进行了多元线性回归分析，以找到驾驶员的个人特征和人格特质，从而可以解释所测驾驶员行为参数中的大多数随机方差。随后，运行主成分分析（PCA）进行测试，以确定是否在模型的第三级上将先前聚类的驾驶员行为参数加载到了假定的行为维度上，以识别驾驶员行为的重要组成部分，例如动力学或法律合规性。本研究的结果显示了驾驶员特性与驾驶员行为参数之间的显着相关性。根据PCA的结果，驾驶员行为的可变性可以在很大程度上由三个基本独立的因素来解释：（1）速度和巡航控制，（ 2）动态和（3）驱动程序性能。通过基于驾驶员行为模拟的驾驶员行为模型中驾驶员个性的考虑，可以提高自动驾驶功能的前瞻性安全影响评估分析结果的有效性。除此之外，考虑到不同的驾驶技能和具有不同个人特征的驾驶员的偏爱，本研究的结果可以用作车辆自动化领域中自适应功能开发的坚实基础。 （C）2019 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号