Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912610121678848 |
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| author | Hasan, Aamir Chakraborty, Neeloy Chen, Haonan Wu, Cathy Driggs-Campbell, Katherine |
| author_facet | Hasan, Aamir Chakraborty, Neeloy Chen, Haonan Wu, Cathy Driggs-Campbell, Katherine |
| contents | Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Until autonomous vehicles mature, such advisory systems will continue to expand their ability to cooperate with drivers, enabling safer and more eco-friendly driving practices while improving user experience. However, the interactions between these systems and drivers have not been studied extensively. To this end, we conduct a driving simulator study (N=16) to capture driver reactions to a Cooperative RTA system. Through a case study with a congestion mitigation assistant, we qualitatively analyze the sentiments of drivers towards advisory systems and discuss driver preferences for various aspects of the interaction. We comment on how the advice should be communicated, the effects of the advice on driver trust, and how drivers adapt to the system. We present recommendations to inform the future design of Cooperative RTA systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study Hasan, Aamir Chakraborty, Neeloy Chen, Haonan Wu, Cathy Driggs-Campbell, Katherine Human-Computer Interaction Artificial Intelligence Real-time Advisory (RTA) systems, such as navigational and eco-driving assistants, are becoming increasingly ubiquitous in vehicles due to their benefits for users and society. Until autonomous vehicles mature, such advisory systems will continue to expand their ability to cooperate with drivers, enabling safer and more eco-friendly driving practices while improving user experience. However, the interactions between these systems and drivers have not been studied extensively. To this end, we conduct a driving simulator study (N=16) to capture driver reactions to a Cooperative RTA system. Through a case study with a congestion mitigation assistant, we qualitatively analyze the sentiments of drivers towards advisory systems and discuss driver preferences for various aspects of the interaction. We comment on how the advice should be communicated, the effects of the advice on driver trust, and how drivers adapt to the system. We present recommendations to inform the future design of Cooperative RTA systems. |
| title | Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2407.13775 |