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Pre-crash scenarios at road junctions: A clustering method for car crash data
Philippe Nitsche1, Pete Thomas2, Rainer Stuetz1
1AIT Austrian Institute of Technology, Giefinggasse 2, 1210 Vienna, Austria.
Accident; Analysis and Prevention
|August 26, 2017
Summary
This study introduces a new method to analyze car crash data at road junctions, identifying critical pre-crash scenarios for testing automated driving systems. The findings help create benchmark situations for safer autonomous vehicle development.
Area of Science:
- Road safety engineering
- Intelligent transportation systems
- Data science in automotive safety
Background:
- Autonomous driving systems require rigorous testing in complex traffic scenarios like road junctions.
- Current testing methods need data-driven approaches to identify critical pre-crash situations effectively.
Purpose of the Study:
- To develop and present a novel data analysis method for identifying critical pre-crash scenarios at T- and four-legged junctions.
- To establish a basis for testing the safety of automated driving systems using historical crash data.
Main Methods:
- Utilized k-medoids clustering to partition historical junction crash data.
- Applied association rules algorithm to each cluster for detailed scenario specification.
- Analyzed and visualized 1056 UK junction crashes from the "On-the-Spot" database.
Main Results:
- Identified thirteen distinct crash clusters for T-junctions and six for crossroads.
- Association rules revealed common crash characteristics defining specific driving scenarios.
- The analysis provided detailed descriptions of critical pre-crash situations at junctions.
Conclusions:
- The developed method effectively identifies critical pre-crash scenarios at road junctions.
- Results support existing knowledge on junction accidents and offer benchmark situations for automated driving system safety tests.
- This approach aids in reducing the complexity of parameter combinations for safety performance evaluations.
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