Driver Sky View Index (DSVI) Data for Urban Driving Safety Analysis

Li Quan's dataset, last updated May 8, 2026, contains results from a study quantifying the impact of environmental complexity on driving safety. The 5.5 KB Excel file likely holds data derived from a virtual reality simulation of nine scenarios, using eye-tracking and driving behavior recordings from 24 participants to calculate the Driver Sky View Index (DSVI).

Use Cases

• Modeling the relationship between visual field metrics and driving load based on the reported R² value of 0.641.
• Identifying optimal urban design thresholds for safety based on the DSVI range of 0.241 to 0.397 mentioned in the results.
• Analyzing driver visual and operational responses to environmental complexity using the described eye-tracking and behavior recording methods.
• Benchmarking virtual reality simulation data against real-world driving scenarios based on the study's methodological approach.

Strengths

• Dataset is small at 5.5 KB, facilitating quick download and inspection.
• Study design is detailed, involving 24 participants, nine VR scenarios, and synchronized eye-tracking and behavior recording.
• Results include a specific, statistically significant linear correlation (R² = 0.641, p < 0.05) between DSVI and driving load.

Limitations

• Row count is unknown, which may limit suitability assessment for machine learning tasks.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's scale is very limited, containing only the study's core results rather than raw observational data.

Provenance

Source

Li Quan, via figshare.

Collection Method

Data generated from a controlled virtual reality simulation experiment using eye-tracking technology and a driving behavior recording system.

Time Range

The simulation study period is not specified.

Freshness

Last updated 2026-05-08 17:30:21; freshness should be verified.

Geography

The geographic context of the urban environments modeled is not specified.