Pavlos Kosmides, K. Demestichas, Konstantinos Avgerinakis, Eleni Trouva, Stefano Bianchi, A. Barisone, Konstantinos Risvas, K. Moustakas, Aleksandra Rodak, M. Kruszewski, Malgorzata Pedzierska
{"title":"为自动驾驶带来信任","authors":"Pavlos Kosmides, K. Demestichas, Konstantinos Avgerinakis, Eleni Trouva, Stefano Bianchi, A. Barisone, Konstantinos Risvas, K. Moustakas, Aleksandra Rodak, M. Kruszewski, Malgorzata Pedzierska","doi":"10.23919/AEITAUTOMOTIVE50086.2020.9307432","DOIUrl":null,"url":null,"abstract":"Last decade has been characterized by a huge advancement in the field of automated and connected transport. However, fully autonomous systems still need a lot of effort in order to be applied in transportation. Meanwhile, mixed traffic environments with semi-autonomous vehicles is becoming a norm. In such conditions, vehicles are passing the dynamic driving task back to the human by sending to drivers Requests to Intervene (RtI). At the same time, there is a need to evolve driver’s training in order to be able to safely use semi-automated vehicles, whereas driver intervention performance has to be made an integral part of both driver and technology assessment. Furthermore, the ethical implications of automated decision-making need to be properly assessed, giving rise to novel risk and liability analysis models. In this conceptual paper we present our vision to maximise the safety, trust and acceptance of automated vehicles. To achieve that, we propose an assessment framework to evaluate different technologies involved in Automated Driving Systems (ADS).","PeriodicalId":104806,"journal":{"name":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Bringing Trust to Autonomous Mobility\",\"authors\":\"Pavlos Kosmides, K. Demestichas, Konstantinos Avgerinakis, Eleni Trouva, Stefano Bianchi, A. Barisone, Konstantinos Risvas, K. Moustakas, Aleksandra Rodak, M. Kruszewski, Malgorzata Pedzierska\",\"doi\":\"10.23919/AEITAUTOMOTIVE50086.2020.9307432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Last decade has been characterized by a huge advancement in the field of automated and connected transport. However, fully autonomous systems still need a lot of effort in order to be applied in transportation. Meanwhile, mixed traffic environments with semi-autonomous vehicles is becoming a norm. In such conditions, vehicles are passing the dynamic driving task back to the human by sending to drivers Requests to Intervene (RtI). At the same time, there is a need to evolve driver’s training in order to be able to safely use semi-automated vehicles, whereas driver intervention performance has to be made an integral part of both driver and technology assessment. Furthermore, the ethical implications of automated decision-making need to be properly assessed, giving rise to novel risk and liability analysis models. In this conceptual paper we present our vision to maximise the safety, trust and acceptance of automated vehicles. To achieve that, we propose an assessment framework to evaluate different technologies involved in Automated Driving Systems (ADS).\",\"PeriodicalId\":104806,\"journal\":{\"name\":\"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307432\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Last decade has been characterized by a huge advancement in the field of automated and connected transport. However, fully autonomous systems still need a lot of effort in order to be applied in transportation. Meanwhile, mixed traffic environments with semi-autonomous vehicles is becoming a norm. In such conditions, vehicles are passing the dynamic driving task back to the human by sending to drivers Requests to Intervene (RtI). At the same time, there is a need to evolve driver’s training in order to be able to safely use semi-automated vehicles, whereas driver intervention performance has to be made an integral part of both driver and technology assessment. Furthermore, the ethical implications of automated decision-making need to be properly assessed, giving rise to novel risk and liability analysis models. In this conceptual paper we present our vision to maximise the safety, trust and acceptance of automated vehicles. To achieve that, we propose an assessment framework to evaluate different technologies involved in Automated Driving Systems (ADS).