The use of volumetric projections in Digital Human Modelling software for the identification of Large Goods Vehicle blind spots

Abstract

The aim of the study is to understand the nature of blind spots in the vision of drivers of Large Goods Vehicles caused by vehicle design variables such as the driver eye height, and mirror designs. The study was informed by the processing of UK national accident data using cluster analysis to establish if vehicle blind spots contribute to accidents. In order to establish the cause and nature of blind spots six top selling trucks in the UK, with a range of sizes were digitized and imported into the SAMMIE Digital Human Modelling (DHM) system. A novel CAD based vision projection technique, which has been validated in a laboratory study, allowed multiple mirror and window aperture projections to be created, resulting in the identification and quantification of a key blind spot. The identified blind spot was demonstrated to have the potential to be associated with the scenarios that were identified in the accident data. The project led to the revision of UNECE Regulation 46 that defines mirror coverage in the European Union, with new vehicle registrations in Europe being required to meet the amended standard after June of 2015.

Keywords: Accident data; Blind spot; Class V mirror; Cluster analysis; Digital human modelling; Field of vision; Heavy goods vehicle; Trucks; Vehicle ergonomics; Vehicles; Vulnerable road user.