Vehicle automation and weather : challenges and opportunities.

Sundararajan, Sudharson; Zohdy, Ismail · 2016 · ROSA P / United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office

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Summary

This report addresses the critical challenges automated vehicles (AVs) face when operating in adverse weather conditions, a gap in current literature that primarily focuses on human driver behavior. While 93% of crashes are attributed to human error and approximately 22% are weather-related, most AV testing occurs in clear conditions. The authors argue that for AVs to gain public acceptance and realize safety benefits, they must demonstrate reliability in rain, snow, fog, and ice. The paper explores the relationship between weather and AV performance, identifies necessary data inputs, and examines how AVs can serve as new sources of road weather data. The study analyzes technical barriers across several domains. Sensor performance is significantly degraded by weather; LiDAR struggles with snow cover, while cameras are impaired by fog, rain, and glare. Although RADAR can penetrate precipitation, it cannot operate independently to ensure safety. Ultrasonic sensors may trigger false alarms from falling snow, and wet or icy surfaces cause reflectivity issues. Operational parameters are also affected, as reduced pavement friction necessitates speed adjustments and increased stopping distances. Furthermore, weather impacts vehicle-to-vehicle and vehicle-to-infrastructure communications by disrupting line-of-sight for visible light communications and increasing latency. The report also highlights a lack of defined technical standards for AV-specific road weather data needs. To mitigate these challenges, the paper identifies specific data requirements, categorized into general weather data (e.g., air temperature, visibility), road weather data (e.g., pavement temperature, friction coefficient, ice buildup), and crowd-sourced observations. It reviews existing resources from the U.S. Department of Transportation’s Road Weather Management Program, specifically the Vehicle Data Translator (VDT) and the Weather Data Environment (WxDE), which provide quality-checked, real-time weather and road condition data. Additionally, the report posits that AVs can enrich these datasets by using their sensors to detect precipitation types, pavement conditions, and hazards like potholes or flooding, thereby improving forecast accuracy and maintenance applications. The significance of this work lies in its roadmap for integrating weather resilience into AV development. The authors conclude that complex solutions and refined data warehouses are necessary for safe AV operation in all conditions. They propose a phased approach: short-term efforts should focus on exploratory research and simulation modeling; medium-term goals include developing robust applications for various pavement conditions and refining standards; and long-term objectives involve integrating weather forecasts into vehicle planning systems and establishing fail-safe approaches for severe weather. This framework aims to accelerate the deployment of interoperable technologies and ensure AVs can safely navigate mixed environments with legacy vehicles.

Key finding

Adverse weather severely degrades automated vehicle sensor and camera performance while disrupting communications, necessitating the integration of high-definition maps and real-time road weather data to ensure safe operation.

Methodology

review

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

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• weather rain fog snow