Paper Info
Title
Probabilistic Prediction of Pedestrian Crossing Intention Using Roadside LiDAR Data
Abstract
Pedestrians are vulnerable road users that need proactive protection. While both autonomous and connected vehicle technologies aim to deliver greater safety benefits, current designs heavily rely on vehicle-based or on-board sensors and lack strategic real-time interactions with pedestrians who do not have any communication means. As pedestrians are passively protected by the system, they might be put into hazardous situations when vehicle-mounted sensors fail to detect their presence. This paper is part of ongoing research that uses roadside light detection and ranging (LiDAR) sensors to develop a human-in-the-loop system that brings pedestrians into the connected environment. To proactively protect pedestrians, accurate prediction of their intention for crossings at locations, such as unsignalized intersections and street mid-blocks is critical, and this paper presents a modified Naive Bayes approach for this purpose. It features a probabilistic approach to overcoming the common deficiencies in deterministic methods and provides valuable comparisons between feature-based data processing methods, such as artificial neural network (ANN) and model-based Naive Bayes approach. A case study was conducted by using a low-cost 16-line LiDAR sensor installed at the roadside. Pedestrians' crossing intention was predicted at a range of 0.5-3 s before actual crossings. The results satisfactorily demonstrated the properties of the modified Naive Bayes model, as well as its higher flexibility, compared with the ANN approaches in practice.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2927889
IEEE ACCESS
Keywords
DocType
Volume
Confidence level,Naive Bayes,pedestrian crossing intention,roadside LiDAR
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Junxuan Zhao141.45
Yinfeng Li200.34
Hao Xu31212.74
Hongchao Liu440.81