Sectional Information-Based Collision Warning System Using Roadside Unit Aggregated Connected-Vehicle Information for a Cooperative Intelligent Transport System

Tak, Sehyun; Yoon, Jinsu; Woo, Seonock; Yeo, Hwasoo

doi:10.1155/2020/1528028

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…As in equation ( 5), the precision is calculated as the ratio of the number of correct answers for vehicle type classification (true positives) to that of all detected vehicles (sum of true and false positives). e recall is calculated as the ratio of the number of correct answers (true positives) to that of all ground truths (sum of true positives and false negatives), as shown in equation (5). Precision and recall are inversely related to each other.…”

Section: Accuracy Of Vehicle Detection and Classificationmentioning

confidence: 99%

“…Particularly in advanced TMSs (ATMSs), real-time collection of precise information through traffic monitoring plays a crucial role for traffic managers when they develop various control strategies [1][2][3]. Furthermore, the detailed numerical status of realtime traffic such as lane-by-lane travel volume and queue length can be used as supplementary information for cooperative intelligent transportation system (C-ITS) operations based on autonomous vehicles [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Tak

Lee

Song³

et al. 2021

Journal of Advanced Transportation

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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.

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Section: Accuracy Of Vehicle Detection and Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Tak

Lee

Song³

et al. 2021

Journal of Advanced Transportation

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“…Tak et al studied a new type of collision warning system that describes the vehicle movement handled by the roadside unit. The proposed collision warning system based on segment information overcomes the limitations of existing collision warning systems (Tak et al, 2020). The above research environment is the urban road traffic environment.…”

Section: Introductionmentioning

confidence: 99%

Research on Agricultural Vehicle Safety Warning System Based on Lidar

KONG

Zhang

et al. 2022

INMATEH

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Intelligent agricultural vehicles have been widely used in the process of farming and harvesting in the field, which has brought great convenience to agricultural production. However, there are also safety issues such as accidental collision of agricultural vehicles or other agricultural machinery during operation. The use of sensing technology for the timely and accurate detection and pre-warning of obstacles during the operation of agricultural machinery is critically important for ensuring safety. In this paper, a two-dimensional lidar is used to detect obstacles in front of tractors with the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm and the Minimum Cost Maximum Flow algorithm(MCMF). A method to judge whether the obstacle is static or dynamic and a classification model of different security warning levels for obstacles in different states is proposed. Actual vehicle tests were conducted, with static obstacles tested repeatedly, and dynamic obstacles tested at different directions and speeds. The results showed that the overall average warning accuracy rate is 89.95%. Prediction results were robust for obstacles in different states, indicating that this system is able to ensure the safety of agricultural vehicles during their operation and promoted the development of agricultural mechanization.

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“…The use of the cloud system in C-ITS was proposed to process and store a massive amount of data generated from various sensors in C-ITS. Different types of data can be collected and analyzed under it, such as traffic flow and link speed data [ 21 ], safety-related data [ 22 ], and emission data [ 23 ]. With the emergence of CVs and CAVs, every vehicle traveling in a traffic network can function as a moving data generator and possibly merge with C-ITS.…”

Section: Introductionmentioning

confidence: 99%

“…As the safety aspects for monitoring are diversified and increased, monitoring the safety performance of the overall network and confronting the possible fallbacks of the automated driving systems of CAVs are the roles that traffic management centers undertake. Certain studies [ 22 , 26 , 27 ] have proposed a TMC-based safety monitoring system that includes data flow. The results show that the TMC-based safety monitoring system provides a good opportunity for improving safety performances, because it can effectively consider the temporal and spatial relationship of the entire road network.…”

Section: Introductionmentioning

confidence: 99%

Safety Monitoring System of CAVs Considering the Trade-Off between Sampling Interval and Data Reliability

Tak

Choi

2022

Sensors

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The safety of urban transportation systems is considered a public health issue worldwide, and many researchers have contributed to improving it. Connected automated vehicles (CAVs) and cooperative intelligent transportation systems (C-ITSs) are considered solutions to ensure the safety of urban transportation systems using various sensors and communication devices. However, realizing a data flow framework, including data collection, data transmission, and data processing, in South Korea is challenging, as CAVs produce a massive amount of data every minute, which cannot be transmitted via existing communication networks. Thus, raw data must be sampled and transmitted to the server for further processing. The data acquired must be highly accurate to ensure the safety of the different agents in C-ITS. On the other hand, raw data must be reduced through sampling to ensure transmission using existing communication systems. Thus, in this study, C-ITS architecture and data flow are designed, including messages and protocols for the safety monitoring system of CAVs, and the optimal sampling interval determined for data transmission while considering the trade-off between communication efficiency and accuracy of the safety performance indicators. Three safety performance indicators were introduced: severe deceleration, lateral position variance, and inverse time to collision. A field test was conducted to collect data from various sensors installed in the CAV, determining the optimal sampling interval. In addition, the Kolmogorov–Smirnov test was conducted to ensure statistical consistency between the sampled and raw datasets. The effects of the sampling interval on message delay, data accuracy, and communication efficiency in terms of the data compression ratio were analyzed. Consequently, a sampling interval of 0.2 s is recommended for optimizing the system’s overall efficiency.

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Sectional Information-Based Collision Warning System Using Roadside Unit Aggregated Connected-Vehicle Information for a Cooperative Intelligent Transport System

Cited by 10 publications

References 25 publications

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Research on Agricultural Vehicle Safety Warning System Based on Lidar

Safety Monitoring System of CAVs Considering the Trade-Off between Sampling Interval and Data Reliability

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