Researchers develop video-based AI traffic light system

Long queues at traffic lights could be a thing of the past, thanks to a new artificial intelligence (AI) system developed by researchers at Aston University, Birmingham.

The AI reads live camera footage and adapts traffic lights to keep vehicles flowing and reduce congestion.

In testing, the system significantly outperformed all other methods, which typically rely on manually designed phase transitions.

“We have set this up as a traffic control game,” said Dr Maria Chli, reader in Computer Science at Aston University.

“The program gets a ‘reward’ when it gets a car through a junction. Every time a car has to wait or there’s a jam, there’s a negative reward. There’s actually no input from us; we simply control the reward system.”

Traffic simulator

Researchers built a state of the art photo-realistic traffic simulator, Traffic 3D, to train their program, teaching it to handle different traffic and weather scenarios.

When the system was tested on a real junction, it adapted to real traffic intersections despite being trained entirely on simulations.

At present, the main form of traffic light automation used at junctions depends on magnetic induction loops – a wire sits on the road and registers cars passing over it.

The program counts that and then reacts to the data. Because the AI ‘sees’ high traffic volume before the cars have gone through the lights and makes its decision, researchers say it is more responsive and can react faster.

Finding links

“The reason we have based this program on learned behaviours is so that it can understand situations it hasn’t explicitly experienced before,” said Dr George Vogiatzis, Senior Lecturer in Computer Science at Aston University.

“We’ve tested this with a physical obstacle that is causing congestion, rather than traffic light phasing, and the system still did well.

“As long as there is a causal link, the computer will ultimately figure out what that link is. It’s an intensely powerful system.”

The program can be set up to view any traffic junction – real or simulated – and will start learning autonomously. The reward system can be manipulated, for example to encourage the program to let emergency vehicles through quickly. But the program always teaches itself, rather than being programmed with specific instructions.

Researchers hope to begin testing their system on real roads this year.

The paper, Fully Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality, is being presented at the Autonomous Agents and Multi-agent Systems Conference 2022, which is being held virtually from 9-13 May.

Image: Aston University