OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User Experience Article Swipe
Pascal Jansen
,
Mark Colley
,
Svenja Krauß
,
Daniel Hirschle
,
Enrico Rukzio
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3706598.3713514
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3706598.3713514
Automated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis's efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.
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- Language
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- Landing Page
- https://doi.org/10.1145/3706598.3713514
- OA Status
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https://openalex.org/W4409750211Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3706598.3713514Digital Object Identifier
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OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User ExperienceWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-24Full publication date if available
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Pascal Jansen, Mark Colley, Svenja Krauß, Daniel Hirschle, Enrico RukzioList of authors in order
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https://doi.org/10.1145/3706598.3713514Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1145/3706598.3713514Direct OA link when available
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Computer science, Bayesian optimization, Human–computer interaction, Bayesian probability, Visualization, Machine learning, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.relies | 4 |
| abstract_inverted_index.trust, | 85 |
| abstract_inverted_index.(N=117) | 78 |
| abstract_inverted_index.broader | 110 |
| abstract_inverted_index.compare | 62 |
| abstract_inverted_index.designs | 32, 69 |
| abstract_inverted_index.enhance | 14 |
| abstract_inverted_index.optimal | 26, 106 |
| abstract_inverted_index.safety, | 88 |
| abstract_inverted_index.vehicle | 1 |
| abstract_inverted_index.without | 91 |
| abstract_inverted_index.Bayesian | 53 |
| abstract_inverted_index.efficacy | 81 |
| abstract_inverted_index.feedback | 59 |
| abstract_inverted_index.optimize | 57 |
| abstract_inverted_index.planning | 22 |
| abstract_inverted_index.stemming | 36 |
| abstract_inverted_index.Automated | 0 |
| abstract_inverted_index.cognitive | 93 |
| abstract_inverted_index.empirical | 39 |
| abstract_inverted_index.enhancing | 102 |
| abstract_inverted_index.feedback. | 9 |
| abstract_inverted_index.improving | 84 |
| abstract_inverted_index.passenger | 107 |
| abstract_inverted_index.perceived | 87 |
| abstract_inverted_index.OptiCarVis | 95 |
| abstract_inverted_index.Warm-Start | 72 |
| abstract_inverted_index.acceptance | 3 |
| abstract_inverted_index.approaches | 50 |
| abstract_inverted_index.conditions | 63 |
| abstract_inverted_index.detection, | 19 |
| abstract_inverted_index.in-vehicle | 103 |
| abstract_inverted_index.increasing | 92 |
| abstract_inverted_index.interfaces | 104 |
| abstract_inverted_index.introduces | 43 |
| abstract_inverted_index.OptiCarVis, | 44 |
| abstract_inverted_index.Traditional | 30 |
| abstract_inverted_index.acceptance, | 86 |
| abstract_inverted_index.experiences | 108 |
| abstract_inverted_index.facilitates | 96 |
| abstract_inverted_index.prediction, | 20 |
| abstract_inverted_index.unsuitable, | 35 |
| abstract_inverted_index.OptiCarVis's | 80 |
| abstract_inverted_index.Optimization | 54 |
| abstract_inverted_index.challenging. | 29 |
| abstract_inverted_index.demonstrates | 79 |
| abstract_inverted_index.establishing | 24 |
| abstract_inverted_index.evaluations. | 40 |
| abstract_inverted_index.exploration, | 101 |
| abstract_inverted_index.comprehensive | 98 |
| abstract_inverted_index.significantly | 83 |
| abstract_inverted_index.understanding | 7, 16 |
| abstract_inverted_index.applicability. | 111 |
| abstract_inverted_index.predictability | 90 |
| abstract_inverted_index.visualizations | 11 |
| abstract_inverted_index.Multi-Objective | 52 |
| abstract_inverted_index.user-customized | 68 |
| abstract_inverted_index.visualizations. | 60 |
| abstract_inverted_index.functionalities, | 23 |
| abstract_inverted_index.Human-in-the-Loop | 48 |
| abstract_inverted_index.resource-intensive | 38 |
| abstract_inverted_index."one-size-fits-all" | 31 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.90397606 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |