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KSU researchers using AI to study collision-prone intersections in state of Georgia

Researchers at Atlanta’s Kennesaw State University (KSU) and the Georgia Department of Transportation (GDOT) are nearing completion on a two-year proof-of-concept study to develop an image-based system for monitoring and assessing the safety of intersections.

The project, which began in January 2015 and is supported by a US$180,000 grant from GDOT, is led by Jidong Yang, assistant professor of civil engineering and director of KSU’s Georgia Pavement and Traffic Research Center; Ying Wang, associate professor of mechatronics engineering, KSU; and Chih-Cheng Hung, professor of computer science, KSU.

Together, they are developing a software package that uses cameras currently installed at major intersections to extract traffic data in real time. The data is then logged in a cloud-based database, which can then be used to help GDOT detect, analyze and correct any potential issues that could lead to vehicle accidents.

The software application would supplement or replace the traditional practice of using historical crash data, which is somewhat ineffective, leaving potentially dangerous intersections to go unnoticed until a significant number of collisions have occurred.

The KSU team and GDOT are taking a more proactive approach by developing a method to monitor intersections before accidents occur. Recent methods have focused on using surrogate measures for crash data, such as ‘conflicts’, which indicate the potential for a crash to occur and can be observed when motorists brake suddenly or swerve to avoid collisions. Humans can be trained to identify conflicts, but it is both costly and unsafe for someone to monitor intersections in person.

Currently, the researchers are in the process of testing their algorithms. Deep learning, which uses a degree of artificial intelligence (AI), will allow the application to identify vehicles entering the intersection and track their positions and trajectory through the camera. The program uses the trajectories extracted from live video to determine the direction and speed of the vehicles as they approach one another, and then determines the risk or probability of a collision.

Over time, the application will log conflicts with a time stamp and information about the risk of a collision. The proposed system will allow GDOT to react in a proactive manner to correct issues, whether it’s by realigning roadways for improved line of sight, adjusting speed limits for better compliance, or by adding additional turn lanes. There may also be room to improve signal timing or to install extra signage or pavement markings for increased visibility.

January 22, 2018

Written by Adam Frost

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