Research news on AI traffic safety

AI traffic safety concerns the use of artificial intelligence to prevent crashes and reduce injuries in road transport systems. Work in this area spans perception and decision-making for autonomous and cooperative driving, AI-enhanced traffic cameras and inspections, and predictive tools for urban planning and enforcement. Human factors are central, including driver stress and vigilance monitoring, cognitive load and takeover performance in semi-autonomous vehicles, moral decision-making in automated systems, and feedback-based interventions that shape safer driving behavior.

Software

Which pothole to fix? AI team helps company develop city system

Artificial intelligence (AI) experts from The University of Texas at Dallas have partnered with a Japanese company through its Irving, Texas-based subsidiary to help local governments prioritize road repairs. The system builds ...

Engineering

Creating the ultimate driver's test for automated vehicles

Automated vehicles have been steadily rolling out in U.S. cities, but scaled deployment still faces a daunting challenge: proving the technology can safely navigate the complexity of real-world driving. Virginia Tech researchers ...

Automotive

The promise of self-driving cars hits a traffic snag

As autonomous vehicles (AVs) near widespread use, researchers at The University of Texas at Arlington say the technology could change not just how people travel but how much, reshaping traffic congestion, city planning and ...

Energy & Green Tech

Researchers measure traffic emissions, to the block, in real-time

In a study focused on New York City, MIT researchers have shown that existing sensors and mobile data can be used to generate a near real-time, high-resolution picture of auto emissions, which could be used to develop local ...

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