Citation

Das S, Tsapakis I, Wei Z, Elgart Z, Kutela B, Vierkant V, Li EPH. Virginia Tech Transportation Institute; San Diego State University, 05-087. College Station, TX USA: Safe-D National UTC, Texas A&M Transportation Institute, 2022.

Copyright

(Copyright 2022, Virginia Tech Transportation Institute; San Diego State University)

The full document is available online.

Abstract

The National Highway Traffic Safety Administration recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these vehicles are occupantless, frequent stops and mobility among residential neighborhoods cause safety- related concerns. There is consequently a need for a comprehensive safety impact analysis of ADVs. This study examined the safety implications and safety impacts of ADVs by using novel approaches. This research prepared several datasets such as fatal crash data, aggregated ADV trips and trajectories, and real-world crash data from the scenario design for an ADV-related operational design domain. Association rules mining was applied to the datasets to identify significant patterns. This study generated a total of 80 association rules that provide risk patterns associated with ADVs. The rules can be used as prospective benchmarks to examine how rule-based risk patterns can be reduced by ADVs that replace human-driven trips.