A Framework for Pedestrian Sub-classification and Arrival Time Prediction at Signalized Intersection Using Preprocessed Lidar Data
This work addresses traffic safety for vulnerable road users, such as disabled pedestrians, by providing real-time detection and prediction capabilities, though it is incremental as it builds on existing sensor and machine learning methods.
The researchers tackled the problem of pedestrian safety at signalized intersections by developing a framework to classify disabled pedestrians and predict their arrival times using preprocessed LiDAR data, achieving high performance in both tasks.
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.