Crash Compatibility of Automated Vehicles with Passenger Vehicles

Dobrovolny, Chiara Silvestri; Stoeltje, Gretchen; Zalani, Aniruddha · 2021 · ROSA P / Safety through Disruption (Safe-D) University Transportation Center (UTC)

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Summary

This research addresses the crash compatibility between no-occupant Automated Driving System (ADS)-equipped vehicles and traditional passenger vehicles. As automated delivery vehicles, such as the Nuro R2, enter public roadways, their distinct body structures—lacking occupant compartments and crumple zones—raise concerns regarding occupant safety in mixed-traffic collisions. The study investigates whether these ADS vehicles, which are often narrower and fully electric, can safely interact with human-driven vehicles without compromising the latter's passive protection systems. The motivation stems from regulatory gaps, as ADS vehicles may be exempt from certain Federal Motor Vehicle Safety Standards, yet they must still ensure geometric and energy absorption compatibility with existing traffic. The methodology employed finite element (FE) computer simulations to model crash scenarios, utilizing vehicle models from George Mason University’s Center for Collision Safety. The study simulated side impacts (targeting the B-pillar and mid-AB pillar) and frontal impacts (moderate and small overlap) involving a Toyota Yaris passenger vehicle and various ADS vehicle prototypes. The ADS models were adapted from traditional vehicle chassis by removing occupant compartments and replacing engines with battery packs, incorporating specific mass and dimensional data for vehicles like the Nuro R2. Impact speeds were set at 25 mph for small ADS vehicles and 30–40 mph for larger variants, reflecting their operational limits. The simulations evaluated structural deformation, intrusion into the occupant compartment, and occupant injury metrics such as Occupant Impact Velocity (OIV) and Ridedown Acceleration (RDA). The results indicate significant differences in crashworthiness behavior compared to passenger-to-passenger collisions. In side impacts, the narrow, bullet-like shape of small ADS vehicles caused higher door deformation and intrusion into the passenger vehicle’s occupant compartment, particularly at the mid-AB pillar location, which yielded marginal safety ratings. The simulations revealed that small ADS vehicles dissipate less kinetic energy through deformation, transmitting more force to the impacted passenger vehicle. In frontal impacts, the lack of a crumple zone in ADS vehicles resulted in increased intrusion into the passenger vehicle’s cabin. The study found that while mid-sized ADS vehicles performed comparably to passenger vehicles, small and large ADS variants posed higher risks of occupant injury due to incompatible energy absorption characteristics and geometric mismatches. The significance of this work lies in its demonstration that current crash testing standards may not adequately address the unique structural properties of no-occupant ADS vehicles. The findings suggest that ADS vehicle designs must explicitly account for crash compatibility with passenger vehicles to prevent compromised occupant safety. The research supports the need for updated design guidelines and passive protection systems for automated vehicles, ensuring that their integration into mixed traffic does not increase injury risks for human drivers. This provides a technical basis for future regulatory considerations and vehicle engineering standards for automated mobility.

Key finding

Small-sized no-occupant ADS-equipped vehicles cause greater intrusion into the occupant compartments of impacted passenger vehicles compared to traditional passenger vehicle collisions, primarily due to their narrow geometry and absence of crumple zones.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
discover success rosap 2 2026-05-23
archive success 1 2026-05-23
extract success cached 2 2026-06-10
clean success 1 2026-06-01
chunk success 1 2026-06-01
embed success 1 2026-06-02
enrich success 1 2026-05-23
promote success 1 2026-05-23
summarize success llm qwen3.6-27b-prismaquant summ-v5 3 2026-06-10
tag success vector_similarity 24 2026-06-11
verify success 2 2026-06-10

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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