Automated Vehicles and Adverse Weather: Final Report

Neumeister, David M.; Pape, Douglas B. · 2019 · ROSA P / United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This report, produced by Battelle for the Federal Highway Administration (FHWA), investigates the impact of adverse weather and road conditions on the performance of automated vehicles (AVs). The research was motivated by the increasing deployment of AVs and the fact that over 20% of annual motor vehicle crashes are weather-related. While manufacturers produce vehicles with sensors and automation features, most current systems are not designed to operate reliably in all adverse conditions. The study aimed to identify AV limitations, opportunities, and shortcomings to inform USDOT research agendas, state agency strategies, and private sector development. The project employed a three-pronged methodology: a comprehensive literature review, controlled experiments with production vehicles, and stakeholder engagement workshops. The literature review analyzed existing technology, sensor gaps, and human factors, noting that Level 2 vehicle manuals explicitly warn against relying on automation in poor visibility or slippery conditions. The experimental phase involved testing five production vehicles with differing perception systems across two test periods simulating spring and winter weather. Conditions included rain, snow, ice on sensors, sun glare, and obscured lane markings. The vehicles performed specific maneuvers, including lane keeping on straight and curved roads, high-speed following, and low-speed traffic jam assist. Additionally, three listening sessions gathered input from government agencies, academia, and industry stakeholders regarding infrastructure support and operational roles. The experiments revealed significant limitations and inconsistencies in AV performance. Level 2 vehicles were successfully challenged by adverse weather, exhibiting substantial variability in performance both between different vehicles and across repeated runs of the same vehicle. Differences in automation approaches, such as sensing methods and algorithm criteria, contributed to these discrepancies. Human factors observations highlighted a lack of consistency in how human-machine interfaces presented status and alerts. Stakeholder engagement identified critical gaps in defining roles and responsibilities, particularly regarding who determines if weather conditions are suitable for automated operation. Infrastructure owners expressed uncertainty about how to support AVs, while manufacturers noted a reliance on onboard perception rather than external data due to limited availability of supplemental weather information. The study concludes that advancements in data connectivity, infrastructure support, and rulemaking are necessary to improve safety and achieve higher automation levels. Key recommendations include establishing metrics to define the weather components of an Operational Design Domain (ODD) and clarifying roles for collecting and disseminating external weather information. The report emphasizes that Level 2 drivers must avoid over-trusting automation and remain prepared to engage, while state and local agencies need better tools to advise on AV use in adverse conditions. Ultimately, the findings highlight the need for coordinated efforts among manufacturers, government, and researchers to ensure AVs can safely navigate weather-related challenges.

Key finding

Level 2 automated vehicles exhibited significant performance inconsistency and limitations when exposed to adverse weather conditions such as rain, snow, and ice.

Methodology

lab_experiment

Sample size: 5

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).

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

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• weather rain fog snow
• situational awareness
• automation surprise
• ehmi external hmi