详情

Enhancing Vehicle Sensing for Traffic Safety Performance Improvements Using Roadside LiDAR Data in RITI Communities

项目名称：	Enhancing Vehicle Sensing for Traffic Safety Performance Improvements Using Roadside LiDAR Data in RITI Communities
摘要：	The Federal Motor Carrier Safety Administration (FMCSA) developed its 2015-2018 strategic plans to identify four strategic focus areas including “Safety 1st” culture and comprehensive data utilization and leveraging technology. Compared to crashes occurring in urban areas, traffic crashes in Rural, Isolated, Tribal, or Indigenous (RITI) communities are associated with a series of significant attributes, such as high speed, low seatbelt usage rate, poor weather and pavement conditions, inferior lighting conditions, considerable distractions, etc. Recent technological advancements in computer vision algorithms and data acquisition devices have greatly facilitated the research to enhance traffic sensing for traffic safety performance improvements. Significant research efforts have been devoted to developing and deploying new technologies to better detect, sense, and monitor traffic dynamics and rapidly identify crashes in RITI communities. This project aims to develop a set of deep learning-based traffic object recognition from roadside Light Detection and Ranging (LiDAR) data to automatically detect vehicle dynamic characteristics, such as vehicle speed, headway, and spacing, and then track vehicle trajectories in real-time to better identify and predict traffic crashes to mitigate crash injuries with minimum response time. Currently, a majority of the existing methodologies applied deep learning-based techniques, especially Convolutional Neural Networks (CNNs), for traffic detection and tracking on autonomous driving datasets. Very fewer studies were focused on deep learning-based traffic detection using roadside LiDAR data, partially due to the lack of publicly available roadside LiDAR datasets for network training and testing. In this study, the research team plans to develop a novel framework based on CNNs and LiDAR data for automated vehicle detection. It leverages the domain knowledge of CNNs trained on large-scale autonomous driving datasets for vehicle detection from roadside LiDAR data. The project results will better use roadside LiDAR data to monitor traffic operation flow conditions and identify crashes dynamically and help transportation agencies improve traffic safety performance developing timely countermeasures in the States of Alaska, Washington, Idaho, and Hawaii.
状态：	Active
资金：	45470
资助组织：	Center for Safety Equity in Transportation；Office of the Assistant Secretary for Research and Technology
管理组织：	Center for Safety Equity in Transportation
执行机构：	University of Hawaii, Manoa
主要研究人员：	Zhang, Guohui；Ma, David T
开始时间：	20220401
预计完成日期：	20230731
主题领域：	Data and Information Technology；Highways；Operations and Traffic Management；Safety and Human Factors