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

Abstract

Vehicular collision and hazard warning is an active field of research that seeks to improve road safety by providing an earlier warning to drivers to help them avoid potential collision danger. In this study, we propose a new type of a collision warning system based on aggregated sectional information, describing vehicle movement processed by a roadside unit (RSU). The proposed sectional information-based collision warning system (SCWS) overcomes the limitations of existing collision warning systems such as the high installation costs, the need for high market penetration rates, and the lack of consideration of traffic dynamics. The proposed SCWS gathers vehicle operation data through on-board units (OBUs) and shares this aggregated information through an RSU. All the data for each road section are locally processed by the RSU using edge computing, allowing the SCWS to effectively estimate the information describing the vehicles surrounding the subject vehicle in each road section. The performance of the SCWS was evaluated through comparison with other collision warning systems such as the vehicle-to-vehicle communication-based collision warning system (VCWS), which solely uses in-vehicle sensors; the hybrid collision warning system (HCWS), which uses information from both infrastructure and in-vehicle sensors; and the infrastructure-based collision warning system (ICWS), which only uses data from infrastructure. In this study, the VCWS with a 100% market penetration rate was considered to provide the most theoretically similar result to the actual collision risk. The comparison results show that in both aggregation and disaggregation level analyses, the proposed SCWS exhibits a similar collision risk trend to the VCWS. Furthermore, the SCWS shows a high potential for practical application because it provides acceptable performance even with a low market penetration rate (30%) at the relatively low cost of OBU installation, compared to the VCWS requirement of a high market penetration rate at a high installation cost.

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

“…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.…”

“…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].…”

Research on Agricultural Vehicle Safety Warning System Based on Lidar

“…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.…”

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

“…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.…”

“…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.…”