1. Optimization of Two-Phase Sampling Designs With Application to Naturalistic Driving Studies
- Author
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Olle Nerman, Henrik Imberg, Vera Lisovskaja, and Selpi Selpi
- Subjects
Optimal design ,Two phase sampling ,Critical event ,business.industry ,Computer science ,Mechanical Engineering ,Simple random sample ,Machine learning ,computer.software_genre ,Computer Science Applications ,Annotation ,Component (UML) ,Automotive Engineering ,Artificial intelligence ,Naturalistic driving ,business ,computer ,Selection (genetic algorithm) - Abstract
Naturalistic driving studies (NDS) generate tremendous amounts of traffic data and constitute an important component of modern traffic safety research. However, analysis of the entire NDS database is rarely feasible, as it often requires expensive and time-consuming annotations of video sequences. We describe how automatic measurements, readily available in an NDS database, may be utilised for selection of time segments for annotation that are most informative with regards to detection of potential associations between driving behaviour and a consecutive safety critical event. The methodology is illustrated and evaluated on data from a large naturalistic driving study, showing that the use of optimised instance selection may reduce the number of segments that need to be annotated by as much as 50%, compared to simple random sampling.
- Published
- 2022