Language of Driving for Autonomous Vehicles. Kalda, K., Pizzagalli, S., Soe, R., Sell, R., & Bellone, M. Applied Sciences, 12(11):5406, January, 2022. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute![Language of Driving for Autonomous Vehicles [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Environmental awareness and technological advancements for self-driving cars are close to making autonomous vehicles (AV) a reality in everyday scenarios and a part of smart cities’ transportation systems. The perception of safety and trust towards AVs of passengers and other agents in the urban scenario, being pedestrians, cyclists, scooter drivers or car drivers, is of primary importance and the theme of investigation of many research groups. Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots. 【摘要翻译】自动驾驶汽车的环境意识和技术进步接近使自动驾驶汽车（AV）在日常情况和智能城市运输系统的一部分中成为现实。在城市场景中，对乘客和其他代理商的安全性和信任的看法是行人，骑自行车的人，踏板车司机或汽车驾驶员，是许多研究小组调查的主题。驾驶员通信渠道与驾驶员人机接口（HMI）一样多，并且是正常培训和经验的一部分。当用户必须应对无人驾驶和自动驾驶汽车时，无论是乘客还是共享相同的城市领域的代理商，情况就会有所不同。这项研究着重于连接无人驾驶汽车的新挑战，研究了新兴主题，即这些机器和人类之间参与交通情况的人（LOD）的语言（LOD）。这项工作介绍了在塔林大学技术校园进行的现场研究结果，其中ISEAUTO自主驾驶班车，包括对使用LOD进行交流的176名受试者的访谈。此外，这项研究结合了专家焦点小组访谈，以建立公共空间中AV的共同需求和需求。根据先前的研究和问卷调查结果，我们确定了假设，我们可以使用具有VR/AR工具的实验场景来增强物理调查结果，以允许对不同的外部和内部HMI的快速原型制作，从而评估沟通效果，评估可用性，可用性，可用性，评估并影响用户。目的是指出我们如何使用XR工具来增强AV设计和LOD通信。选择这些方案是包容性的，并支持不同人口统计学的需求，同时确定在LOD测试和设计中为未来飞行员设计的调查和现实世界实验场景的局限性。
@article{kalda_language_2022,
title = {Language of {Driving} for {Autonomous} {Vehicles}},
volume = {12},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {2076-3417},
shorttitle = {驾驶自动驾驶的语言},
url = {https://www.mdpi.com/2076-3417/12/11/5406},
doi = {10.3390/app12115406},
abstract = {Environmental awareness and technological advancements for self-driving cars are close to making autonomous vehicles (AV) a reality in everyday scenarios and a part of smart cities’ transportation systems. The perception of safety and trust towards AVs of passengers and other agents in the urban scenario, being pedestrians, cyclists, scooter drivers or car drivers, is of primary importance and the theme of investigation of many research groups. Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots.
【摘要翻译】自动驾驶汽车的环境意识和技术进步接近使自动驾驶汽车（AV）在日常情况和智能城市运输系统的一部分中成为现实。在城市场景中，对乘客和其他代理商的安全性和信任的看法是行人，骑自行车的人，踏板车司机或汽车驾驶员，是许多研究小组调查的主题。驾驶员通信渠道与驾驶员人机接口（HMI）一样多，并且是正常培训和经验的一部分。当用户必须应对无人驾驶和自动驾驶汽车时，无论是乘客还是共享相同的城市领域的代理商，情况就会有所不同。这项研究着重于连接无人驾驶汽车的新挑战，研究了新兴主题，即这些机器和人类之间参与交通情况的人（LOD）的语言（LOD）。这项工作介绍了在塔林大学技术校园进行的现场研究结果，其中ISEAUTO自主驾驶班车，包括对使用LOD进行交流的176名受试者的访谈。此外，这项研究结合了专家焦点小组访谈，以建立公共空间中AV的共同需求和需求。根据先前的研究和问卷调查结果，我们确定了假设，我们可以使用具有VR/AR工具的实验场景来增强物理调查结果，以允许对不同的外部和内部HMI的快速原型制作，从而评估沟通效果，评估可用性，可用性，可用性，评估并影响用户。目的是指出我们如何使用XR工具来增强AV设计和LOD通信。选择这些方案是包容性的，并支持不同人口统计学的需求，同时确定在LOD测试和设计中为未来飞行员设计的调查和现实世界实验场景的局限性。},
language = {en},
number = {11},
urldate = {2022-12-06},
journal = {Applied Sciences},
author = {Kalda, Krister and Pizzagalli, Simone-Luca and Soe, Ralf-Martin and Sell, Raivo and Bellone, Mauro},
month = jan,
year = {2022},
note = {Number: 11
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {/reading, AV shuttle, interaction, language of driving, self-driving vehicle, simulations},
pages = {5406},
}
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Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots. 【摘要翻译】自动驾驶汽车的环境意识和技术进步接近使自动驾驶汽车（AV）在日常情况和智能城市运输系统的一部分中成为现实。在城市场景中，对乘客和其他代理商的安全性和信任的看法是行人，骑自行车的人，踏板车司机或汽车驾驶员，是许多研究小组调查的主题。驾驶员通信渠道与驾驶员人机接口（HMI）一样多，并且是正常培训和经验的一部分。当用户必须应对无人驾驶和自动驾驶汽车时，无论是乘客还是共享相同的城市领域的代理商，情况就会有所不同。这项研究着重于连接无人驾驶汽车的新挑战，研究了新兴主题，即这些机器和人类之间参与交通情况的人（LOD）的语言（LOD）。这项工作介绍了在塔林大学技术校园进行的现场研究结果，其中ISEAUTO自主驾驶班车，包括对使用LOD进行交流的176名受试者的访谈。此外，这项研究结合了专家焦点小组访谈，以建立公共空间中AV的共同需求和需求。根据先前的研究和问卷调查结果，我们确定了假设，我们可以使用具有VR/AR工具的实验场景来增强物理调查结果，以允许对不同的外部和内部HMI的快速原型制作，从而评估沟通效果，评估可用性，可用性，可用性，评估并影响用户。目的是指出我们如何使用XR工具来增强AV设计和LOD通信。选择这些方案是包容性的，并支持不同人口统计学的需求，同时确定在LOD测试和设计中为未来飞行员设计的调查和现实世界实验场景的局限性。","language":"en","number":"11","urldate":"2022-12-06","journal":"Applied Sciences","author":[{"propositions":[],"lastnames":["Kalda"],"firstnames":["Krister"],"suffixes":[]},{"propositions":[],"lastnames":["Pizzagalli"],"firstnames":["Simone-Luca"],"suffixes":[]},{"propositions":[],"lastnames":["Soe"],"firstnames":["Ralf-Martin"],"suffixes":[]},{"propositions":[],"lastnames":["Sell"],"firstnames":["Raivo"],"suffixes":[]},{"propositions":[],"lastnames":["Bellone"],"firstnames":["Mauro"],"suffixes":[]}],"month":"January","year":"2022","note":"Number: 11 Publisher: Multidisciplinary Digital Publishing Institute","keywords":"/reading, AV shuttle, interaction, language of driving, self-driving vehicle, simulations","pages":"5406","bibtex":"@article{kalda_language_2022,\n\ttitle = {Language of {Driving} for {Autonomous} {Vehicles}},\n\tvolume = {12},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tissn = {2076-3417},\n\tshorttitle = {驾驶自动驾驶的语言},\n\turl = {https://www.mdpi.com/2076-3417/12/11/5406},\n\tdoi = {10.3390/app12115406},\n\tabstract = {Environmental awareness and technological advancements for self-driving cars are close to making autonomous vehicles (AV) a reality in everyday scenarios and a part of smart cities’ transportation systems. The perception of safety and trust towards AVs of passengers and other agents in the urban scenario, being pedestrians, cyclists, scooter drivers or car drivers, is of primary importance and the theme of investigation of many research groups. Driver-to-driver communication channels as much as car-to-driver human–machine interfaces (HMI) are well established and part of normal training and experience. The situation is different when users must cope with driverless and autonomous vehicles, both as passengers and as agents sharing the same urban domain. This research focuses on the new challenges of connected driverless vehicles, investigating an emerging topic, namely the language of driving (LoD) between these machines and humans participating in traffic scenarios. This work presents the results of a field study conducted at Tallinn University Technology campus with the ISEAUTO autonomous driving shuttle, including interviews with 176 subjects communicating using LoD. Furthermore, this study combines expert focus group interviews to build a joint base of needs and requirements for AVs in public spaces. Based on previous studies and questionnaire results, we established the hypotheses that we can enhance physical survey results using experimental scenarios with VR/AR tools to allow the fast prototyping of different external and internal HMIs, facilitating the assessment of communication efficacy, evaluation of usability, and impact on the users. The aim is to point out how we can enhance AV design and LoD communications using XR tools. The scenarios were chosen to be inclusive and support the needs of different demographics while at the same time determining the limitations of surveys and real-world experimental scenarios in LoD testing and design for future pilots.\n\n【摘要翻译】自动驾驶汽车的环境意识和技术进步接近使自动驾驶汽车（AV）在日常情况和智能城市运输系统的一部分中成为现实。在城市场景中，对乘客和其他代理商的安全性和信任的看法是行人，骑自行车的人，踏板车司机或汽车驾驶员，是许多研究小组调查的主题。驾驶员通信渠道与驾驶员人机接口（HMI）一样多，并且是正常培训和经验的一部分。当用户必须应对无人驾驶和自动驾驶汽车时，无论是乘客还是共享相同的城市领域的代理商，情况就会有所不同。这项研究着重于连接无人驾驶汽车的新挑战，研究了新兴主题，即这些机器和人类之间参与交通情况的人（LOD）的语言（LOD）。这项工作介绍了在塔林大学技术校园进行的现场研究结果，其中ISEAUTO自主驾驶班车，包括对使用LOD进行交流的176名受试者的访谈。此外，这项研究结合了专家焦点小组访谈，以建立公共空间中AV的共同需求和需求。根据先前的研究和问卷调查结果，我们确定了假设，我们可以使用具有VR/AR工具的实验场景来增强物理调查结果，以允许对不同的外部和内部HMI的快速原型制作，从而评估沟通效果，评估可用性，可用性，可用性，评估并影响用户。目的是指出我们如何使用XR工具来增强AV设计和LOD通信。选择这些方案是包容性的，并支持不同人口统计学的需求，同时确定在LOD测试和设计中为未来飞行员设计的调查和现实世界实验场景的局限性。},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2022-12-06},\n\tjournal = {Applied Sciences},\n\tauthor = {Kalda, Krister and Pizzagalli, Simone-Luca and Soe, Ralf-Martin and Sell, Raivo and Bellone, Mauro},\n\tmonth = jan,\n\tyear = {2022},\n\tnote = {Number: 11\nPublisher: Multidisciplinary Digital Publishing Institute},\n\tkeywords = {/reading, AV shuttle, interaction, language of driving, self-driving vehicle, simulations},\n\tpages = {5406},\n}\n\n","author_short":["Kalda, K.","Pizzagalli, S.","Soe, R.","Sell, R.","Bellone, M."],"key":"kalda_language_2022","id":"kalda_language_2022","bibbaseid":"kalda-pizzagalli-soe-sell-bellone-languageofdrivingforautonomousvehicles-2022","role":"author","urls":{"Paper":"https://www.mdpi.com/2076-3417/12/11/5406"},"keyword":["/reading","AV shuttle","interaction","language of driving","self-driving vehicle","simulations"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/_MingYAN_","dataSources":["7bvyz9GrWb3E3F8y3"],"keywords":["/reading","av shuttle","interaction","language of driving","self-driving vehicle","simulations"],"search_terms":["language","driving","autonomous","vehicles","kalda","pizzagalli","soe","sell","bellone"],"title":"Language of Driving for Autonomous Vehicles","year":2022}