Abstract
There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.
Highlights
Urban road traffic is a complex phenomenon caused by interactions among various moving entities, such as vehicles and pedestrians. e growth in urban population during the past decades has raised the severity of urban traffic congestion, leading to socioeconomic and environmental problems in modern cities
Overall, based on the analyses of the five different evaluation criteria, the method proposed in this study shows the feasibility of collecting detailed traffic information with a camera installed at an intersection
We considered a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. e method uses a deep-learning-based approach for image processing for vehicle detection and vehicle type classification. e method estimates the lane-by-lane vehicle trajectories using the detected locations of vehicles
Summary
Urban road traffic is a complex phenomenon caused by interactions among various moving entities, such as vehicles and pedestrians. e growth in urban population during the past decades has raised the severity of urban traffic congestion, leading to socioeconomic and environmental problems in modern cities. Indirect methods estimate traffic status such as travel volume and travel time within a road section based on the data samples collected via roadside units (RSU) or global positioning systems (GPS), which are instances of automatic vehicle identification (AVI) technologies [6,7,8]. Automatic traffic data collection via camera-based monitoring systems can be operated at lower costs only when proper image processing techniques support the system. With the current machine-learning-based image processing techniques, a possibility of detecting multiple vehicles as a single object arises when they travel through similar paths and speeds, even though on different road lanes. E work in [40] had a similar purpose and approach but differs from the present study in that recent deep-learning techniques and HD map technology are combined for estimating vehicle positions accurately.
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