Study on the Framework of Intersection Pedestrian Collision Warning System Considering Pedestrian Characteristics

Abstract

One of the most widely used advanced driver assistance systems (ADAS) for preventing pedestrian–vehicle collisions is the intersection collision warning system (ICWS). Most previous ICWSs have been implemented with in-vehicle distance sensors, such as radar and lidar. However, the existing ICWSs show some weaknesses in alerting drivers at intersections because of limited detection range and field-of-view. Furthermore, these ICWSs have difficulties in identifying the pedestrian’s crossing intention because the distance sensors cannot capture pedestrian characteristics such as age, gender, and head orientation. To alleviate these defects, this study proposes a novel framework for vision sensor-based ICWS under a cloud-based communication environment, which is called the intersection pedestrian collision warning system (IPCWS). The IPCWS gives a collision warning to drivers approaching an intersection by predicting the pedestrian’s crossing intention based on various machine learning models. With real traffic data extracted by image processing in the IPCWS, a comparison study is conducted to evaluate the performance of the IPCWS in relation to warning timing. The comparison study demonstrates that the IPCWS shows better performance than conventional ICWSs. This result suggests that the proposed system has a great potential for preventing pedestrian–vehicle collisions by capturing the pedestrian’s crossing intention.

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

“…Despite this, when pedestrians are partially hidden, there is a drop in performance. With the same approach, several systems capable of detecting pedestrians while crossing a zebra crossing have been proposed [ 18 , 19 ]. They combine cameras and machine learning techniques such as region-based convolutional neural networks (CNN), vector support machines (SVM) or multilayer perceptron neural networks (MLP).…”

Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network

“…It could be too late for drivers to take evasive actions when the pedestrian was already in the FOV. Thus, more work should be done to capture and predict pedestrians’ jaywalking intentions ( 14 , 15 ).…”

“…As there are many irregularities in pedestrians’ movements, video data could be employed to capture and analyze pedestrians’ characteristics from a microscopic view. Video data were used in previous work to investigate pedestrians’ crossing behaviors ( 2 , 6 , 7 , 11 , 15 , 16 ). Zaki and Sayed investigated pedestrians’ spatial traffic violations, that is, the pedestrian was not walking in the designated region, and temporal violations, that is, the pedestrian crossed during an improper traffic signal phase ( 2 ).…”

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

“…The detection of pedestrians and animals on the road, to improve road safety, has also been studied. This is the case of systems designed to detect the intention to cross the road and alert drivers through cameras [ 29 , 30 , 31 ] and LIDAR technologies [ 32 ] applying dense, recurrent, or convolutional neural networks [ 33 ]. Other work focuses on the detection of pedestrians on zebra crossings using cameras and different classification schemes such as Haarcascade, histogram of oriented gradients (HOG), single-shot detector (SSD), and you-only-look-once (YOLO) [ 34 ].…”