WRSR Distribution Table: Driving Safety Metrics from VR and Eye-Tracking Experiments

Li Quan's WRSR distribution table, last updated on 2026-05 08, contains results from a study on the impact of urban environmental complexity on driving safety. The 5.5 KB Excel file likely holds composite scores from the Entropy Weight–Rank Sum Ratio (EW-RSR) algorithm, applied to data from 24 participants in nine virtual reality driving scenarios. The study introduced the Driver Sky View Index (DSVI) to quantify environmental complexity and measured its correlation with driving load (R²=0.641).

Use Cases

• Modeling the relationship between environmental complexity (DSVI) and driving safety metrics based on the described EW-RSR composite scores.
• Analyzing thresholds for safety improvement based on the identified DSVI range of 0.241 to 0.397 mentioned in the description.
• Correlating visual field metrics with operational driving performance based on the integration of eye-tracking and driving behavior data.
• Benchmarking virtual reality simulation outputs for driver safety studies based on the described nine-scenario experimental design.

Strengths

• Includes a specific, novel metric (Driver Sky View Index) for quantifying urban environmental complexity.
• Data is derived from a controlled experiment with 24 participants using synchronized eye-tracking and driving behavior recording.
• Results show a statistically significant linear correlation (R²=0.641, p<0.05) between DSVI and driving load.
• Identifies a specific DSVI value range (0.241 to 0.397) associated with marked safety improvement.

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 is very small (5.5 KB), indicating limited scope, likely containing summary or composite scores rather than raw trial data.

Provenance

Source

Li Quan, published on figshare.

Collection Method

Data was generated via a virtual reality simulation of nine driving scenarios, with participant visual and operational responses captured using eye-tracking technology and a driving behavior recording system.

Time Range

The experimental time frame is not specified.

Freshness

Last updated 2026-05-08 17:30:22; freshness should be verified.

Geography

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