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Prediction Tool Developed by UK Researchers Featured in TU-Automotive Article

November 21, 2018

The tool attempts to predict how many accidents could be avoided by using autonomous vehicles instead of human drivers based on a wide variety of factors.

The website TU-Automotive published an article about a prediction tool being developed by researchers in the University of Kentucky Department of Civil Engineering and the Kentucky Transportation Center.

Professor Reg Souleyrette and graduate students Austin Obenauf and Freddy Lause have been working on a tool called a tool called Data-Driven Safety Assessment for Connected and Autonomous Transportation (ddSAFCAT). The tool attempts to predict how many accidents could be avoided by using autonomous vehicles instead of human drivers based on a wide variety of factors.

The article captures excellent work going on within an extremely high-profile field. You can read it here. 

Error

November 21, 2018

The tool attempts to predict how many accidents could be avoided by using autonomous vehicles instead of human drivers based on a wide variety of factors.

The website TU-Automotive published an article about a prediction tool being developed by researchers in the University of Kentucky Department of Civil Engineering and the Kentucky Transportation Center.

Professor Reg Souleyrette and graduate students Austin Obenauf and Freddy Lause have been working on a tool called a tool called Data-Driven Safety Assessment for Connected and Autonomous Transportation (ddSAFCAT). The tool attempts to predict how many accidents could be avoided by using autonomous vehicles instead of human drivers based on a wide variety of factors.

The article captures excellent work going on within an extremely high-profile field. You can read it here.