Crash Simulations Between Non-Occupied Automated Driving Systems and Roadside Hardware
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Summary
This report addresses the safety implications of non-occupied Automated Driving System (ADS) vehicles interacting with roadside hardware. As ADS technology advances, vehicles designed solely for cargo or delivery without occupants may no longer be subject to Federal Motor Vehicle Safety Standards focused on occupant protection. This shift raises concerns about how these vehicles, which may differ significantly in mass, size, and structural design from traditional vehicles, interact with existing roadside safety devices. The study aims to understand the crash characteristics and kinematics of these occupantless ADS vehicles when striking roadside hardware, identifying potential differences in performance compared to traditional vehicles to inform future safety regulations and hardware design. The research was conducted by the Center for Collision Safety and Analysis at George Mason University using finite element (FE) crash simulations. The team developed generic FE models for four non-occupied ADS vehicle concepts, ranging from small delivery vehicles to large tractor-trailer combinations, based on previously validated traditional vehicle models. These ADS models featured "skateboard-type" chassis and modular bodies. The simulations evaluated impacts against five representative roadside hardware devices: vertical curbs, U-post sign supports, slip-base sign supports, W-beam guardrails, and New Jersey concrete barriers. The study utilized a design of experiment approach, varying impact velocities (40–88 km/h) and angles (20–30 degrees) to reflect anticipated operational design domains and automated steering capabilities of ADS vehicles. The simulations provided initial findings on the crash performance of non-occupied ADS vehicles against various roadside devices. The study analyzed vehicle response, including stability and penetration, noting that traditional evaluation criteria, such as the requirement for vehicles to remain upright to protect occupants, may not be relevant for occupantless ADS vehicles. The results highlighted that crash outcomes are strongly correlated with specific impact conditions and vehicle configurations. The generic FE models allowed for the evaluation of a wide range of scenarios, revealing how different vehicle masses and structural characteristics influence the interaction with roadside hardware. The study established a baseline for understanding how ADS vehicles behave in unavoidable collisions, providing data on vehicle kinematics and hardware response under diverse impact parameters. The significance of this work lies in its contribution to the development of safety standards and roadside hardware designs for the emerging era of automated driving. By identifying differences in crash behavior between ADS and traditional vehicles, the findings support the need for updated evaluation protocols that account for occupantless vehicle dynamics. The report concludes that while ADS technology may reduce crash frequency, unavoidable collisions will still occur, necessitating robust roadside safety infrastructure. The developed simulation models and initial findings provide a foundation for future research, including the integration of field data and the development of machine learning tools to help ADS vehicles manage hazardous scenarios and minimize crash severity.
Key finding
Crash patterns of non-occupied automated driving systems differ significantly from traditional vehicles, with impact outcomes strongly correlating to specific vehicle mass, velocity, and angle conditions.
Methodology
simulator
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).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| 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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- Empirical Findings: crash risk outcomes