A nonlinear mixed logit model of occupant severity in autonomous vehicle crashes

Abstract

This paper presents a nonlinear mixed logit to capture heterogeneous effects of contributing factors on autonomous involved occupant severity. Autonomous level information to this point has been quite sparse in the context of real-world crash scenarios and police reporting. However, the Texas Department of Transportation (TxDOT) began reporting autonomous involvement in April of 2023. With reporting still in its early stages, this analysis incorporated three distinct vehicle technologies: non-autonomous internal combustion engine (ICE) vehicles; ICE and hybrid electric autonomous vehicles; and fully electric autonomous vehicles. Crash data included any crash in Texas from April to December of 2023 that involved at least one autonomous-indicated vehicle (either the second or third distinct vehicle technology). Random parameters were found with respect to: an indicator for occupant involvement in the first harmful crash sequence event, with that event being collision with a fixed object, for no injury; proportion of autonomous vehicles for no injury; an intersection related indicator for possible injury; total occupant count for possible injury; and total vehicle count for injury. The count and proportion variables were expressed as nonlinear relationships, for which random parameters improved prediction accuracy by 37.50 % and 30.00 %, respectively, for possible injury and injury outcomes, as compared to fixed parameters. The findings in this study highlight the applicability of the nonlinear mixed logit for severity analysis with respect to complex autonomous interactions in crashes.