AI-IoT test solution for pedestrian traffic safety

26th November 2019
Source: Tieto
Posted By : Alex Lynn
AI-IoT test solution for pedestrian traffic safety

The City of Tampere and Tieto have developed a solution to improve the urban traffic safety of pedestrians. Utilising artificial intelligence (AI) and Internet of Things (IoT) technology, the test solution can automatically detect when a pedestrian is planning to cross the street at an intersection. 

An alert can be relayed to automatic traffic signs, and in the future even directly to vehicles. Furthermore, in the future, the system can also be a building block for autonomous vehicles.

Urbanisation increases the number of people on the move in metropolitan areas. As road traffic increases, so does the risk of traffic accidents, especially at intersections. The risk of injury and death is especially high for pedestrians.

To increase the safety of urban traffic and prevent accidents, the City of Tampere and Tieto built a pilot solution that utilises AI and IoT technology. Developed as a part of the Smart Tampere development program, the pilot solution can help reduce pedestrian and vehicle accidents in urban intersections. The solution is built in a way which prevents the identification of individual people or vehicles.

In Tampere, an intersection traffic camera feed was connected to a cloud-based AI system which monitors vehicles and pedestrians. When the system’s algorithms detect that a pedestrian is beginning to cross the street, it gives an alert. This alert can be relayed to other connected systems, such as automatic traffic signs. In the future, with vehicles becoming increasingly digital, the alert could be relayed directly to vehicles to alert drivers. 

“By enhancing existing traffic monitoring technology with artificial intelligence, we can better identify traffic accident risks,” said Jari Torkkola, Program Director, Tieto Product Development Services. “The system monitors the movements of vehicles and pedestrians, and recognises when a pedestrian intends to cross the street. Especially in areas of limited visibility, the system can help prevent accidents.”

The results are promising. Under ideal conditions, the system achieves 99% accuracy. Even at night accuracy was high (75%).

“We had identified the most common types of accidents between vehicles and pedestrians. Using them, we built an algorithm that can predict the movement of vehicles and pedestrians on the street,” added Pekka Stenman, Traffic Engineer, City of Tampere. “The new solution has many potential uses in addition to boosting traffic safety. We already receive information about vehicle traffic, but not very much about pedestrian traffic. We want to see how people move, and perhaps construct heat maps of Tampere's pedestrian flows to assist with traffic planning. Another interesting opportunity is introducing more intelligence to traffic lights by identifying and predicting people flows.” 

“This implementation also provided one piece in the puzzle of autonomous vehicle systems. A critical question is how self-driving vehicles are able to recognise and avoid obstacles. This type of pedestrian recognition system could be an important element in the safety of autonomous vehicles in urban areas,” Torkkola finished.


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