论文标题
使用预处理的LIDAR数据在信号交叉点进行行人子分类和到达时间预测的框架
A Framework for Pedestrian Sub-classification and Arrival Time Prediction at Signalized Intersection Using Preprocessed Lidar Data
论文作者
论文摘要
使用轮椅的行人的死亡率比总人口行人死亡率高36％。但是，没有数据可以澄清行人在致命事故和非致命事故中的类别，因为警方报告通常不会记录受害者是使用轮椅还是残疾。当前，使用在基础设施方面安装的高级交通传感器对弱势道路使用者的实时检测具有很大的潜力，可以显着提高交叉路口的交通安全性。在这项研究中，我们开发了一个系统的框架，结合了机器学习和深度学习模型，以将残疾人与正常行人行人区分开，并预测到达交叉路口的下一侧所需的时间。所提出的框架在脆弱的用户分类和到达时间预测准确性时显示出高性能。
The mortality rate for pedestrians using wheelchairs was 36% higher than the overall population pedestrian mortality rate. However, there is no data to clarify the pedestrians' categories in both fatal and nonfatal accidents, since police reports often do not keep a record of whether a victim was using a wheelchair or has a disability. Currently, real-time detection of vulnerable road users using advanced traffic sensors installed at the infrastructure side has a great potential to significantly improve traffic safety at the intersection. In this research, we develop a systematic framework with a combination of machine learning and deep learning models to distinguish disabled people from normal walk pedestrians and predict the time needed to reach the next side of the intersection. The proposed framework shows high performance both at vulnerable user classification and arrival time prediction accuracy.
