BIGDATA: IA: Predictive Analytics of Driver's Engagement for Injury Prevention

美国每年有3万多人死于车祸。驾驶员，特别是年轻驾驶员，从次要的车内活动中分心，已成为机动车碰撞的主要原因。大量的研究已经集中在分析自然驾驶或模拟数据的小集合上，以研究用于检测驾驶员的参与的少量特征。然而，许多有意义的依赖关系和模式只能通过大量数据来发现。在这个项目中，目标是利用两PB的自然驾驶数据联邦数据库来开发预测分析，用于检测驾驶员脱离驾驶任务的情况，以便向驾驶员提供警报并降低机动车碰撞的风险。本计画针对战略公路研究计画二（SHRP 2）中超过100个变数的大量异质资料，探讨资料预处理技术。基于实例学习和异构网络挖掘的预测建模的可扩展的预测分析算法家族的开发。此外，一个新的分布式计算基础设施，以支持可扩展的预测分析在驾驶行为分析，建模和预测的模式挖掘的发展。本项目的研究成果对当前机动车碰撞伤害预防工作具有重要意义。该项目扩展了机器学习、传感器信息学和驾驶行为分析的能力。综合教育计划包括将研究成果纳入健康信息学理学硕士课程。外联计划包括组织讲习班、会议和研讨会，以传播研究成果。

Over 30,000 people are killed in motor vehicle crashes on US roadways every year. Driver distraction from secondary in-vehicle activities, particularly among young drivers, has emerged as a major cause of motor vehicle crashes. A substantial amount of research has been focused on analyzing small sets of naturalistic driving or simulated data to study a small number of features for detecting the driver's engagement. However, many meaningful dependencies and patterns can only be discovered by large collections of data. In this project, the goal is leveraging the two petabytes federal database of naturalistic driving data to develop predictive analytics for detecting a driver's disengagement from the driving tasks in order to provide alerts to drivers and reduce the risk of motor vehicle crashes. In this project, data pre-processing techniques are investigated for the large volume of heterogeneous data with over 100 variables in Strategic Highway Research Program 2 (SHRP 2). Two scalable predictive analytics algorithm families based on instance-based learning and heterogeneous network mining for predictive modeling are developed. In addition, a novel distributed computing infrastructure to support the scalable predictive analytics in performing pattern mining of driving behavior analysis, modeling, and prediction are developed. The research outcomes of this project shed a significant amount insight into current work of injury prevention due to motor vehicle crashes. The project extends the capability of machine learning, sensor informatics, and driving behavior analytics. The integrated education plan includes incorporating the research findings in courses offered at the Master of Science program in Health Informatics. The outreach plan involves organizing workshops, conferences, and seminars to disseminate the research outcomes.