Integrated Transport Systems With Artificial Intelligence Control
Year: 2025
Type: conference-paper
Abstract: : City traffic gets more ridiculous every year. that never end, people driving like bumper cars, and don’t even get me started on the fumes. The old-school traffic lights those fixed timers or whatever “smart” sensors they claim to have just don’t keep up when everyone suddenly heads to the same brunch spot or a random rainy-day jam everything up. But here’s the wild part the wizards in tech have whipped up deep reinforcement learning (DRL picture computers learning when to make you sit or go) plus cars that talk to each other (CAVs for the acronym nerds). Basically, brains in the intersections. This review dives into how the newest DRL powered traffic lights try to juggle all three headaches at once speed, safety, and not choking on exhaust. the researchers are obsessed with things like D3QN, DQN (don’t worry about the alphabet soup), and these squad-like multi-agent setups, which all seem to cut down on wait times, pollution. Is there’s still a bunch to do like, weaving all those goals together without tripping over their own wires, and nobody’s totally nailed rolling this stuff out in actual, messy cities with real people yet. The paper flags a bunch of holes where research still needs to catch up and tosses out thoughts on how to make this tech work in the wild, not just on some fancy simulation.
Author: Sharma, Atul Kumar
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7091638336
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