Driver Trust Calibration Data for Voice Agent Tone and Gender in Automated Driving

Raw experimental data supporting a study on how anthropomorphic conversational agent tone style and gender modulate driver trust in Level 2–3 automated driving. The dataset, authored by Xian Yang and last updated on 2026-05-05, contains all raw and processed data required to replicate findings from two core experiments. The compressed package is 7.1 MB in size.

Use Cases

• Replicate trust calibration experiments based on the described study design.
• Analyze driver responses to gentle versus direct conversational agent tone styles.
• Investigate the impact of agent gender attributes on driver trust in automated driving scenarios.
• Study human-vehicle interaction in Level 2–3 automated driving contexts using experimental data.

Strengths

• Data is provided under a CC-BY-4.0 license, facilitating open reuse and sharing.
• Includes all raw and processed data required to replicate the study's findings.
• Dataset size is 7.1 MB, making it relatively small and easy to download and manage.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Data collected from two core experiments on driver interaction with conversational agents in automated driving scenarios.

Freshness

Last updated 2026-05-05 10:35:56; freshness should be verified.