Ubiquitous Data Mining and Situation-Awareness for Improving Road Safety

交通事故每年给澳大利亚造成150亿美元的损失，其中95%是由于司机的失误造成的。风险评估是道路碰撞问题的核心。这个创新的项目开发了一个计算模型，可以分析情景司机的行为，并提出实时对策，以将死亡/伤亡降至最低。我们开发和评估了一种新型的智能交通系统，该系统可以评估司机的风险并对其采取行动。这个多学科项目整合了数据挖掘、情景感知计算、生理指标、无所不在的计算、驾驶员分心模型、风险感知和道路安全的最新模型。该项目对驾驶员在危险情况下的行为和对策有了新的理解。

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.