Lane-Changing Risk Assessment Model with Driving-Behavior Uncertainty

Wenbin Yao's research proposes a lane-changing risk assessment model that incorporates driving-behavior uncertainty. The model is evaluated using lane-changing samples extracted from the NGSIM and highD datasets. Results indicate the model performs better than four common benchmarks at identifying instantaneous risks and predicting following vehicle deceleration.

Use Cases

• Benchmarking lane-changing risk models based on the proposed risk attenuation function.
• Analyzing the impact of driving-behavior uncertainty on traffic safety based on the model's methodology.
• Training or validating autonomous driving systems based on the described risk assessment framework.
• Studying the relationship between acceleration scenarios and lane-changing risk based on the definite integral computation method.

Strengths

• Model is evaluated against four commonly used lane-changing risk assessment indicators or models.
• Dataset is derived from established public sources, specifically the NGSIM and highD datasets.
• Files are provided in accessible formats (PDF, DOCX) totaling 1.2 MB.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset appears to contain model documentation and analysis rather than raw observational data.

Provenance

Source

figshare, authored by Wenbin Yao.

Collection Method

A subset of lane-changing samples extracted from the NGSIM and highD datasets.

Time Range

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Freshness

Last updated 2026-05-15 15:00:08; freshness should be verified.

Geography

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