An omni-directional model of injury risk in planar crashes with application for autonomous vehicles. (19th October 2021)

• Record Type: Journal Article
• Authors: McMurry, Timothy L.; Cormier, Joseph M.; Daniel, Tom; Scanlon, John M.; Crandall, Jeff R.
• Is Part Of: Traffic injury prevention. Volume 22:Supplement 1(2021)
• Page Start: S122
• Page End: S127
• Publication Date: 2021-10-19
• Subjects: Automated driving systems -- injury risk function -- NASS-CDS -- CISS; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2021.1955108
• Languages: English
• ISSNs: 1538-9588

Abstract

Abstract: Objective: Automated driving systems (ADS) are actively being deployed within the driving fleet. ADS are designed to safely navigate roadways, which entails an expectation of encountering varying degrees of potential conflict with other road users. The ADS design and evaluation process benefits from estimating injury severity probabilities for collisions that may occur. Current regression models in the literature are typically bespoke analyses involving targeted principal directions of force (PDOFs) and occupant positions. It is preferable to rely on injury severity models derived from a single source to provide a continuous function of risk for all planar collisions, while also accounting for specific vehicle and occupant characteristics. The novel feature of the proposed models is continuous, parametric injury risk surfaces that encompass the full spectrum of available United States field data. Methods: We used years 2001–2015 of the National Automotive Sampling System, Crashworthiness Data System (NASS-CDS) and years 2017–2019 of the Crash Investigation Sampling System (CISS) to estimate injury risk at the maximum abbreviated injury scale (MAIS) 3 and higher (3+) and 5 and higher (5+) levels for all adult occupants traveling in 2002 or newer passenger vehicles which were less than 10 years old at the time of the crash. The models account for occupant, vehicle, and crash characteristics. Interactions with vulnerable road users (e.g., pedestrian, bicyclist) were not considered. Results: We present statistical models suitable to predict injury in all non-rollover crashes at the maximum MAIS3+ and 5+ levels, and show that these models can be comparable to similar single scenario (e.g., frontal) crash models. We discuss challenges with imputing missing field data, and discuss handling of covariates that may not be known at the time of the crash. Conclusions: Collision severity assessment is a vital component of the ADS design process. We developed a novel injury risk function that can assess occupant injury risks across the spectrum of foreseeable planar collisions. These models can provide insight on potential outcomes of counterfactual simulations, injury risk and crashworthiness considerations for human driven vehicles, and provide an evaluation tool that can be applied in ADS safety impact evaluation.