AI traffic system sooner a reality in Europe
Scientists are experimenting with using Artificial Intelligence (AI) to manage traffic signals to enhance traffic flow, decrease wait times, and increase pedestrian safety at crosswalks.
The EU estimates that traffic delays cost its members €100 billion each year. Fraunhofer researchers have obtained funds from the German Federal Ministry of Transport and Digital Infrastructure to enhance traffic flow.
The rigidity of traditional traffic signals does not operate in all scenarios. Also, current sensors like induction loops embedded in the road surface only provide a vague idea of actual traffic flow. The researchers employed high-resolution cameras and radar sensors to capture the actual traffic condition, enabling precise real-time counting of automobiles at a junction. They found that artificial intelligence might increase traffic flow by 10–15% at the busy Lemgo intersection with intelligent lighting.
The system also monitors average automobile speed and wait times. Real-time sensors and AI replace rigid control rules, and Deep Reinforcement Learning (DRL) methods are used to identify intelligent solutions. The algorithms developed in this manner compute the appropriate phase sequence for traffic signals to cut waiting times at intersections, reduce trip times, and reduce noise and CO2 pollution produced by queued vehicles.
An edge computer in the junction control box runs AI algorithms. The algorithms may be tested, deployed, and scaled up to incorporate lights in a more extensive network. The study will now look at how traffic measurements affect characteristics like noise and emissions. The team believes there would be an almost zero gap between their simulation and real-life traffic flows.
Experiement and Result
A Lemgo’s junction was used to construct a realistic simulation and train the AI on numerous model iterations. Before starting the simulation, the model used actual data from rush hour traffic. This resulted in a deep reinforcement learning agent: a neural network representing light control.
The simulation’s assumptions concerning traffic behaviour are not exact replicas of reality. So the agent must be changed. If this works, the repercussions will be immense. Consider the sheer number of traffic signals in a town like Lemgo.
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