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.

Automotive

Real-time driver warning system helps prevent animal collisions

It's 9 p.m. on a rural mountain highway when a 750-pound (340-kilogram) elk steps onto the road, its silhouette hidden from an oncoming driver by a curve in the terrain. Suddenly, a roadside sign illuminates: "Wildlife Detected ...

Electronics & Semiconductors

New platform tackles critical material risks in electric motors

As manufacturers face growing uncertainty tied to critical material shortages and global supply chain disruptions, the Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) researchers have developed a super intelligence ...

Automotive

Using AI to respond more quickly to traffic hazards

As the BMW test vehicle approaches the intersection, the artificial intelligence system has already gathered information from all relevant sensors in the vicinity. These include infrastructure sensors mounted on the sign ...

Robotics

How should automated vehicles interact with pedestrians?

How do pedestrians react when they encounter an automated vehicle on the street? Does the interaction feel safe and comfortable, and does it make a difference whether the vehicle is operating autonomously, is driven by a ...

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