{"title":"基于协同感知信息的C-ITS安全评价方法","authors":"Mohammed Elhenawy, A. Bond, A. Rakotonirainy","doi":"10.1109/ITSC.2018.8569417","DOIUrl":null,"url":null,"abstract":"Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. Evaluation of their safety benefits in Field Operational Tests (FOT) is needed to demonstrate its benefits and build public awareness and uptake. The result of the evaluation can tell us if the C-ITS algorithms that trigger the safety events, and consequently the Human-Machine Interface (HMI) messages, are appropriately fine-tuned to induce the expected driver behavior change formulated as hypothesis. In this paper we will introduce the safety hypotheses for seven driver safety use cases being deployed by the Queensland Department of Transport and Main Roads in the Ipswich Connected Vehicle Pilot in Australia. The safety performance indicators to test these hypotheses will be introduced as well. The main challenge in evaluating the safety benefits is only using the information collected from C-ITS units without augmentation from any other sensors. To validate data collection, we ran an experiment at the Mt Cotton training facility close to Brisbane and collected Cooperative Awareness Message (CAM) messages to analyze them and check whether the speed and acceleration information extracted from them is accurate enough to detect change in speed and braking. The analysis results show that we can detect the change in speed, but the acceleration/braking pattern is very noisy and needs careful manipulation to retrieve the braking behavior.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"C-ITS Safety Evaluation Methodology based on Cooperative Awareness Messages\",\"authors\":\"Mohammed Elhenawy, A. Bond, A. Rakotonirainy\",\"doi\":\"10.1109/ITSC.2018.8569417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. Evaluation of their safety benefits in Field Operational Tests (FOT) is needed to demonstrate its benefits and build public awareness and uptake. The result of the evaluation can tell us if the C-ITS algorithms that trigger the safety events, and consequently the Human-Machine Interface (HMI) messages, are appropriately fine-tuned to induce the expected driver behavior change formulated as hypothesis. In this paper we will introduce the safety hypotheses for seven driver safety use cases being deployed by the Queensland Department of Transport and Main Roads in the Ipswich Connected Vehicle Pilot in Australia. The safety performance indicators to test these hypotheses will be introduced as well. The main challenge in evaluating the safety benefits is only using the information collected from C-ITS units without augmentation from any other sensors. To validate data collection, we ran an experiment at the Mt Cotton training facility close to Brisbane and collected Cooperative Awareness Message (CAM) messages to analyze them and check whether the speed and acceleration information extracted from them is accurate enough to detect change in speed and braking. The analysis results show that we can detect the change in speed, but the acceleration/braking pattern is very noisy and needs careful manipulation to retrieve the braking behavior.\",\"PeriodicalId\":395239,\"journal\":{\"name\":\"2018 21st International Conference on Intelligent Transportation Systems (ITSC)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 21st International Conference on Intelligent Transportation Systems (ITSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSC.2018.8569417\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2018.8569417","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
C-ITS Safety Evaluation Methodology based on Cooperative Awareness Messages
Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. Evaluation of their safety benefits in Field Operational Tests (FOT) is needed to demonstrate its benefits and build public awareness and uptake. The result of the evaluation can tell us if the C-ITS algorithms that trigger the safety events, and consequently the Human-Machine Interface (HMI) messages, are appropriately fine-tuned to induce the expected driver behavior change formulated as hypothesis. In this paper we will introduce the safety hypotheses for seven driver safety use cases being deployed by the Queensland Department of Transport and Main Roads in the Ipswich Connected Vehicle Pilot in Australia. The safety performance indicators to test these hypotheses will be introduced as well. The main challenge in evaluating the safety benefits is only using the information collected from C-ITS units without augmentation from any other sensors. To validate data collection, we ran an experiment at the Mt Cotton training facility close to Brisbane and collected Cooperative Awareness Message (CAM) messages to analyze them and check whether the speed and acceleration information extracted from them is accurate enough to detect change in speed and braking. The analysis results show that we can detect the change in speed, but the acceleration/braking pattern is very noisy and needs careful manipulation to retrieve the braking behavior.