Fundamental Study on Human Model Synthesis Based on Extraction ofAccident Risk Variable and Its State Transition Probability

基于事故风险变量及其状态转移概率提取的人体模型合成基础研究

• 批准号: 23656180
• 负责人: NAGAI Masao
• 金额: $ 2.5万
• 依托单位: Tokyo University of Agriculture and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Challenging Exploratory Research
• 财政年份: 2011
• 资助国家: 日本
• 起止时间: 2011 至 2012
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23656180/
• 关键词: ドライバモデル; 予防安全研究; 個別適合; 運転行動分析; 人間機械ｼｽﾃﾑ; 予防安全; 自動車工学; ｼﾐｭﾚｰｼｮﾝ工学; モデル化

This research proposes a theoretical method of by driver-vehicle system model parameter analysis and driving operational behavior analysis especially in drowsiness driving state and inattention states. This research potentially contributes to the further reduction of rear-end collision accidents which accounts for 30% of all accidents in Japan. The following research issues were conducted during the research term:(1) The method to extract driving behavior parameters which relate to the velocity control based on experiments using Driving Simulator and a test car.(2) Public expressway driving data collection between Chofu city and Kawaguchiko town using a number of subject drivers.(3) Drowsiness driving behavior analysis by comparing the driving data with the driver model.(4) Verification of the effectiveness of the drowsiness detection method by comparing with the subjective evaluation results using driver face camera image.The velocity-control driver model used in this study is expressed as a spring-mass-damper system based on risk potential theory.Furthermore, by including the lateral-control driver model, the higher accuracy of drowsiness driving detection can be obtained. The achievement from this researchis expected to be applied to active safety systems to prevent accidents.

本研究提出了一种基于人车系统模型参数分析和驾驶行为分析的理论方法，特别是在困倦驾驶状态和注意力不集中驾驶状态下。这项研究可能有助于进一步减少追尾事故，追尾事故占日本所有事故的30%。研究期间主要进行了以下方面的研究：(1)基于驾驶模拟器和试验车的试验，提取与速度控制相关的驾驶行为参数的方法。(2)利用多名主体驾驶员采集调阜市至川口子镇之间的公共高速公路行车数据。(3)通过驾驶数据与驾驶员模型的对比分析瞌睡驾驶行为。(4)通过与驾驶员面部摄像头图像主观评价结果对比，验证困倦检测方法的有效性。本文采用的速度控制驱动模型是基于风险潜势理论的弹簧-质量-阻尼系统。此外，通过加入横向控制驾驶员模型，可以获得更高的嗜睡驾驶检测精度。该研究成果有望应用于预防事故的主动安全系统。

项目成果

Model-Based Estimation Method of Driver Inattention State During Car-Following Situations

基于模型的跟驰情况下驾驶员注意力不集中状态估计方法

• 发表时间: 2012
• 作者: Shintaro Saigo;Raksincharoensak Pongsathorn;Masao Nagai
• 通讯作者: Masao Nagai

Estimation of Driving Performance Level Using Longitudinal and Lateral Driver Models

使用纵向和横向驾驶员模型估计驾驶性能水平

• 发表时间: 2013
• 作者: Shintaro Saigo;Raksincharoensak Pongsathorn;Masao Nagai
• 通讯作者: Masao Nagai

「先行車追従時の規範ドライバモデルに基づく個別適合型運転状態診断システムの開発」として

“在跟随前车时基于标准驾驶员模型开发个性化驾驶状态诊断系统”

• 发表时间: 2011
• 期刊: 自動車技術会論文集
• 作者: 西郷慎太朗,ホ゜ンサトーン・ラクシンチャラーンサク,永井正夫

Autonomous Driving Intelligence System to Enhance Safe and Secured Traffic Society for Elderly Drivers

自动驾驶智能系统，为老年驾驶员打造安全有保障的交通社会

• 发表时间: 2013
• 作者: Shintaro Saigo;Raksincharoensak Pongsathorn;Masao Nagai;Masao Nagai
• 通讯作者: Masao Nagai

Investigation of Inattentive Driving Estimation Method by Using Longitudinal and Lateral Driver Operational Models

利用纵向和横向驾驶员操作模型研究疏忽驾驶估计方法

• 发表时间: 2013
• 作者: Shintaro Saigo;Raksincharoensak Pongsathorn;Masao Nagai
• 通讯作者: Masao Nagai