University researchers have used AI to revolutionise the way in which motor vehicles accidents can be predicted.

The team led by researchers from the United States’ Johns Hopkins University described the system as “a significant step towards improving road safety.”

The A.I. -based tool can identify the risk factors contributing to car crashes across the United States and to accurately predict future incidents.

The tool, SafeTraffic Copilot, aims to provide experts with both crash analyses and crash predictions to reduce the rising number of fatalities and injuries that happen on U.S. roads each year.

“Car crashes in the U.S. continue to increase, despite decades of countermeasures, and these are complex events affected by numerous variables, like weather, traffic patterns, and driver behaviour,” said senior author Hao (Frank) Yang, a professor of civil and systems engineering. “With SafeTraffic Copilot, our goal is to simplify this complexity and provide infrastructure designers and policymakers with data-based insights to mitigate crashes.”

The team uses Large Language Models (LLMs) which are designed to process, understand, and learn from vast amounts of data. SafeTraffic Copilot was trained using text (i.e., descriptions of road conditions), numerical values (i.e., blood alcohol levels), satellite images and on-site photography. The team’s model also has the ability to evaluate both individual and combined risk factors, offering a more detailed understanding of how these elements interact to influence crashes.

Using the system researchers say they can quantify the trustworthiness of the prediction with a given prediction 70% accurate in a real-world scenario.

“By reframing crash prediction as a reasoning task and using LLMs to integrate written and visual data, the stakeholders can move from coarse, aggregate statistics, to a fine-tuned understanding of what causes specific crashes,” Yang said.

The model gives policymakers and transportation designers a trustworthy and interpretable tool to identify combinations of factors that elevate crash risk. The data can then be used to execute evidence-based interventions and more effective infrastructure planning to save lives and reduce injuries.

The researchers see the model as a copilot for human decision-making.

“Rather than replacing humans, LLMs should serve as copilots – processing information, identifying patterns, and quantifying risks – while humans remain the final decision-makers,” Yang added.

“SafeTraffic Copilot has the potential to be a blueprint for responsibly integrating AI-based models into high-stakes fields, like public health and human safety,” the team added. “Because LLMs operate as large black-box models, users do not know how predictions are generated, deterring their use in high-risk decision-making scenarios.”

“The central focus of our ongoing research is to find the best way to combine the strengths of humans and LLMs so that decisions in high-stakes domains are not only data-driven, but also transparent, accountable, and aligned with societal values,” Yang added.