Ubiquitous Data Mining and Situation-Awareness for Improving Road Safety

无处不在的数据挖掘和态势感知可改善道路安全

• 批准号: LP0560865
• 负责人: Prof Andry Rakotonirainy
• 金额: $ 6.52万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2005
• 资助国家: 澳大利亚
• 起止时间: 2005-07-20 至 2010-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP0560865
• 关键词: Ubiquitous; Data; Mining; Situation; Awareness

Road crashes cost Australia $15 billion a year and 95% of these are attributed to drivers' errors. Risk assessment is at the core of the road crash problem. This innovative project develops a computational model that analyses situational driver behaviour and proposes real-time countermeasures to minimise fatalities/casualties. We develop and evaluate a novel Intelligent Transport System that assesses and acts upon drivers' risks. This multidisciplinary project integrates recent models of data mining, context-awareness computing, physiological metrics, ubiquitous computing, driver distraction models, risk perception and road safety. This project yields a new understanding of driver behaviour and countermeasures in risk situations.

道路交通事故每年给澳大利亚造成150亿澳元的损失，其中95%是由于司机的失误。风险评估是道路碰撞问题的核心。这个创新项目开发了一个计算模型，分析情境驾驶员行为，并提出实时对策，以最大限度地减少死亡/伤亡。 我们开发和评估一种新的智能交通系统，评估和行动后，司机的风险。这个多学科项目集成了数据挖掘，上下文感知计算，生理指标，无处不在的计算，驾驶员分心模型，风险感知和道路安全的最新模型。该项目产生了对驾驶员行为和风险情况下对策的新理解。