Special Issue Information Sciences

Fundamental Study on Detection of Dangerous Objects on the Road Surface Leading to Motorcycle Accidents Using a 360-Degree Camera

In recent years, the number of fatalities in traffic accidents involving motorcyclists has remained almost unchanged, with single-vehicle accidents accounting for 37.2% of all accidents by accident type in the past five years. In the development of overturn prevention devices for motorcycles, problems remain in post-mounting of the device as well as its downsizing. On the other hand, an existing study using deep learning has proposed a method for detecting dangerous objects on the road surface leading motorcycles to overturn, though this method still needs verification under different conditions. In this study, we apply a method for detecting dangerous objects on the road surface from video images using YOLO to two types of 360-degree cameras and verify that this method is versatile under different conditions.

View more >>

Special Issue Others

A Study on the Development of a Traffic Volume Counting Method by Vehicle Type and Direction Using Deep Learning

The turning movement count is investigated to understand the traffic conditions at intersections and identify bottleneck locations. In recent years, methods utilizing probe data and AI-based analysis of video images have been developed to streamline the survey process. Existing methods can count vehicles as they pass but struggle to classify vehicle types. Therefore, the objective of this study is to develop a method for counting turning movement count by vehicle type using deep learning. In this method, YOLOv8 is used to detect cars, buses, and trucks in video images, and BoT-SORT is used for tracking. When a vehicle being tracked crosses the cross-sectional lines and auxiliary lines at the intersection captured in the video images, it is counted by class. In this case, the entry direction of vehicles that cannot be determined upon entering the intersection is estimated based on accurately counted vehicles. Additionally, the entry direction is inferred from a series of vector information within the detection bounding boxes. The results of the verification experiment showed that the proposed method can count the directional traffic volume with an accuracy of over 95.0% and classify the three vehicle classes—car, bus, and truck—with an accuracy of over 90.0%.

View more >>

Article Information Sciences

Measurement of Motor-vehicle Traffic Volume Using Camera Images and Artificial Intelligence

In Japan, road traffic censuses are conducted to assess road traffic conditions. Recently, techniques for counting traffic volume from video images have been attracting considerable attention in order to improve work efficiency and save labor, and a large number of technologies have been developed. However, since traffic volume surveys are often conducted 24 hours a day, day and night, at various sites and under various weather conditions, existing technologies have yet to reach the counting accuracy required in practice. The authors aim to develop techniques for traffic volume surveys applicable in practice by applying artificial intelligence. This paper reports the results of a case study in which the proposed techniques were applied to the video taken during actual traffic volume surveys.

View more >>