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

每年有超过3万人死于美国公路上的机动车撞车事故。司机对车内次要活动的分心，特别是在年轻司机中，已成为机动车撞车的主要原因。大量的研究集中在分析少量的自然驾驶或模拟数据，以研究用于检测驾驶员接合的少量特征。然而，许多有意义的依赖项和模式只能通过大型数据集合来发现。在这个项目中，目标是利用两千万亿字节的自然驾驶数据联邦数据库来开发预测性分析，以检测司机从驾驶任务中解脱出来，以便向司机提供警报，并降低机动车碰撞的风险。本课题针对战略公路研究计划2(SHRP 2)中包含100多个变量的海量异质数据进行了数据预处理技术的研究。提出了基于实例学习和基于预测建模的异质网络挖掘两种可扩展的预测分析算法系列。此外，还开发了一种新的分布式计算基础设施，以支持可扩展的预测分析，以执行驾驶行为分析、建模和预测的模式挖掘。本项目的研究成果对当前的机动车碰撞伤害预防工作具有重要的借鉴意义。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卫生信息学理科硕士课程。外展计划包括组织研讨会、会议和研讨会来传播研究成果。

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.