TY - GEN
T1 - Traffic and Transport Ergonomics on Long Term Multi-Agent Social Interactions
T2 - 24th International Conference on Human-Computer Interaction, HCII 2022
AU - Mbelekani, Naomi Y.
AU - Bengler, Klaus
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - There are public debates concerning the meaning highly automated vehicles (HAVs) will possess in society and on road social interactions, with the topic of autonomy being frequently stressed. The de-contextualised and abstract discussion has influenced the development of the on-road Multi-Agent Social Interaction (MASI) concept. By addressing socially sensitive road users’ co-experiences with HAVs, a range of social situations, and how the social dimensions comes to play in different future scenarios may aid scholars and manufacturers gain insights as well as challenge perceived assumptions. For prolific future mobility research and development, we need to consider long-term automation effects to on-road social interactive behaviours – an under-addressed and often overlooked issue. Thus, we revised ideas that infer social interaction patterns, in hopes to try to interrogate and situate these patterns within the context of MASI. These viewpoints are compared and integrated to formulate a structured and explorative framework, describing MASI in the context of space sharing, as this is important to consider in the early stage of HAV development. This paper aims to situate the experiences of road users within an illustrative urban context, as real-world commuting experiences may include multiple people interacting with HAVs at the same time. Further offers significant contributions, which is a description of “MASI”: in short, as on-road social interaction consisting of different road users (humans and automated vehicles), as agents that socially interact with and react to each other, simultaneously. In essence, a classification of co-experiences that users’ may exhibit in in-group social interactions with HAV and interaction dynamics with automation tailored for different road-user types, and further supports several multimodalities. This framework is helpful in envisioning better-equipped systems, models, theories, human factors requirements for interaction designed strategies, and in-group-out-group humans-automation collaborative research.
AB - There are public debates concerning the meaning highly automated vehicles (HAVs) will possess in society and on road social interactions, with the topic of autonomy being frequently stressed. The de-contextualised and abstract discussion has influenced the development of the on-road Multi-Agent Social Interaction (MASI) concept. By addressing socially sensitive road users’ co-experiences with HAVs, a range of social situations, and how the social dimensions comes to play in different future scenarios may aid scholars and manufacturers gain insights as well as challenge perceived assumptions. For prolific future mobility research and development, we need to consider long-term automation effects to on-road social interactive behaviours – an under-addressed and often overlooked issue. Thus, we revised ideas that infer social interaction patterns, in hopes to try to interrogate and situate these patterns within the context of MASI. These viewpoints are compared and integrated to formulate a structured and explorative framework, describing MASI in the context of space sharing, as this is important to consider in the early stage of HAV development. This paper aims to situate the experiences of road users within an illustrative urban context, as real-world commuting experiences may include multiple people interacting with HAVs at the same time. Further offers significant contributions, which is a description of “MASI”: in short, as on-road social interaction consisting of different road users (humans and automated vehicles), as agents that socially interact with and react to each other, simultaneously. In essence, a classification of co-experiences that users’ may exhibit in in-group social interactions with HAV and interaction dynamics with automation tailored for different road-user types, and further supports several multimodalities. This framework is helpful in envisioning better-equipped systems, models, theories, human factors requirements for interaction designed strategies, and in-group-out-group humans-automation collaborative research.
KW - Automated vehicles
KW - Human factors
KW - Long-term automation effects
KW - MASI
KW - Traffic and transport ergonomics
KW - User experiences
UR - http://www.scopus.com/inward/record.url?scp=85142728964&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18158-0_36
DO - 10.1007/978-3-031-18158-0_36
M3 - Conference contribution
AN - SCOPUS:85142728964
SN - 9783031181573
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 499
EP - 518
BT - HCI International 2022 – Late Breaking Papers
A2 - Rauterberg, Matthias
A2 - Fui-Hoon Nah, Fiona
A2 - Siau, Keng
A2 - Krömker, Heidi
A2 - Wei, June
A2 - Salvendy, Gavriel
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 June 2022 through 1 July 2022
ER -