{"title":"CAI2M2:适用于异构互联车辆的集中式自主包容交叉路口管理机制","authors":"Ashkan Gholamhosseinian;Jochen Seitz","doi":"10.1109/OJVT.2024.3354393","DOIUrl":null,"url":null,"abstract":"This paper introduces a novel centralized autonomous inclusive intersection management mechanism (CAI\n<sup>2</sup>\nM\n<sup>2</sup>\n) for heterogeneous connected vehicles (HCVs). The system embraces a diverse array of human-driven vehicles, each possessing unique characteristics. The proposed system navigates vehicles through the intersection safely and efficiently considering various road conditions including dry (D), wet (W), snowy (S), and icy (I). The communication relies on dedicated short-range communications (DSRC) to facilitate the seamless exchange of traffic information between roadside unit (RSU) and vehicles. The coordination policy takes into account parameters such as vehicle types, arrival times, intersection rules, road priorities, and prevailing road conditions. To enhance safety and prevent collisions, vehicles are classified based on distinctive safety features and dynamics, such as reaction distance (\n<inline-formula><tex-math>${d_{r}}$</tex-math></inline-formula>\n), stopping distance (\n<inline-formula><tex-math>${d_{s}}$</tex-math></inline-formula>\n), braking distance (\n<inline-formula><tex-math>${d_{b}}$</tex-math></inline-formula>\n), braking lag distance (\n<inline-formula><tex-math>${d_{bl}}$</tex-math></inline-formula>\n), acceleration (\n<inline-formula><tex-math>$acc.$</tex-math></inline-formula>\n), deceleration (\n<inline-formula><tex-math>$dec.$</tex-math></inline-formula>\n), load, and velocity (\n<inline-formula><tex-math>$v$</tex-math></inline-formula>\n). The paper evaluates the system performance through metrics encompassing average travel time (ATT), packet loss rate (PLR), throughput, intersection busy time (IBT), and channel busy rate (CBR) across several traffic scenarios with different densities and distribution patterns. Additionally, the study compares the system efficiency with signalized intersections under various road conditions, aiming to identify an optimal control approach for autonomous intersection management","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"230-243"},"PeriodicalIF":5.3000,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400835","citationCount":"0","resultStr":"{\"title\":\"CAI2M2: A Centralized Autonomous Inclusive Intersection Management Mechanism for Heterogeneous Connected Vehicles\",\"authors\":\"Ashkan Gholamhosseinian;Jochen Seitz\",\"doi\":\"10.1109/OJVT.2024.3354393\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a novel centralized autonomous inclusive intersection management mechanism (CAI\\n<sup>2</sup>\\nM\\n<sup>2</sup>\\n) for heterogeneous connected vehicles (HCVs). The system embraces a diverse array of human-driven vehicles, each possessing unique characteristics. The proposed system navigates vehicles through the intersection safely and efficiently considering various road conditions including dry (D), wet (W), snowy (S), and icy (I). The communication relies on dedicated short-range communications (DSRC) to facilitate the seamless exchange of traffic information between roadside unit (RSU) and vehicles. The coordination policy takes into account parameters such as vehicle types, arrival times, intersection rules, road priorities, and prevailing road conditions. To enhance safety and prevent collisions, vehicles are classified based on distinctive safety features and dynamics, such as reaction distance (\\n<inline-formula><tex-math>${d_{r}}$</tex-math></inline-formula>\\n), stopping distance (\\n<inline-formula><tex-math>${d_{s}}$</tex-math></inline-formula>\\n), braking distance (\\n<inline-formula><tex-math>${d_{b}}$</tex-math></inline-formula>\\n), braking lag distance (\\n<inline-formula><tex-math>${d_{bl}}$</tex-math></inline-formula>\\n), acceleration (\\n<inline-formula><tex-math>$acc.$</tex-math></inline-formula>\\n), deceleration (\\n<inline-formula><tex-math>$dec.$</tex-math></inline-formula>\\n), load, and velocity (\\n<inline-formula><tex-math>$v$</tex-math></inline-formula>\\n). The paper evaluates the system performance through metrics encompassing average travel time (ATT), packet loss rate (PLR), throughput, intersection busy time (IBT), and channel busy rate (CBR) across several traffic scenarios with different densities and distribution patterns. Additionally, the study compares the system efficiency with signalized intersections under various road conditions, aiming to identify an optimal control approach for autonomous intersection management\",\"PeriodicalId\":34270,\"journal\":{\"name\":\"IEEE Open Journal of Vehicular Technology\",\"volume\":\"5 \",\"pages\":\"230-243\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400835\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Vehicular Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10400835/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Vehicular Technology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10400835/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
CAI2M2: A Centralized Autonomous Inclusive Intersection Management Mechanism for Heterogeneous Connected Vehicles
This paper introduces a novel centralized autonomous inclusive intersection management mechanism (CAI
2
M
2
) for heterogeneous connected vehicles (HCVs). The system embraces a diverse array of human-driven vehicles, each possessing unique characteristics. The proposed system navigates vehicles through the intersection safely and efficiently considering various road conditions including dry (D), wet (W), snowy (S), and icy (I). The communication relies on dedicated short-range communications (DSRC) to facilitate the seamless exchange of traffic information between roadside unit (RSU) and vehicles. The coordination policy takes into account parameters such as vehicle types, arrival times, intersection rules, road priorities, and prevailing road conditions. To enhance safety and prevent collisions, vehicles are classified based on distinctive safety features and dynamics, such as reaction distance (
${d_{r}}$
), stopping distance (
${d_{s}}$
), braking distance (
${d_{b}}$
), braking lag distance (
${d_{bl}}$
), acceleration (
$acc.$
), deceleration (
$dec.$
), load, and velocity (
$v$
). The paper evaluates the system performance through metrics encompassing average travel time (ATT), packet loss rate (PLR), throughput, intersection busy time (IBT), and channel busy rate (CBR) across several traffic scenarios with different densities and distribution patterns. Additionally, the study compares the system efficiency with signalized intersections under various road conditions, aiming to identify an optimal control approach for autonomous intersection management