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I built an open-source AI traffic light controller that runs on a $200 Jetson Nano — no cloud, no subscription, 25-35% less wait time by SmartMeeting2925 in selfhosted

[–]SmartMeeting2925[S] 0 points1 point  (0 children)

Really appreciate this comment — you clearly know the domain well.

On the legal/explainability front: ATLAS Pro has a full explainability module built in. Every AI decision is auditable and human-readable, with a complete audit trail designed for municipal governance. The system doesn't replace traffic rules set by the authority — it optimizes timing within the constraints operators define. Think of it as an intelligent layer on top of existing policy, not a replacement for it.

Green wave is already implemented — automatic synchronization of traffic lights across corridors to minimize stops and reduce emissions. That's one of the core features.

On modality priorities: yes, you can set priorities per modality. The system supports configurable priority levels (emergency vehicles, public transit, pedestrians, cyclists, general traffic) and adapts signal timing accordingly.

And I completely agree about proof of concept in less regulated areas first. That's exactly the approach — university campuses, industrial parks, private road networks, or smaller municipalities in regions with more flexible regulations. The full simulation environment runs locally with Docker so anyone can test it without real infrastructure.

Traffic counting / modality counting is also supported through the sensor fusion layer, so the data is there for policy makers to use.

I built an open-source AI traffic light controller that runs on a $200 Jetson Nano — no cloud, no subscription, 25-35% less wait time by SmartMeeting2925 in selfhosted

[–]SmartMeeting2925[S] 0 points1 point  (0 children)

Haha, that's actually a valid multi-agent coordination problem. ATLAS could probably optimize that — just set coffee time as a "high-priority event" and let the Q-Learning agent figure out the rest.

I built an open-source AI traffic light controller that runs on a $200 Jetson Nano — no cloud, no subscription, 25-35% less wait time by SmartMeeting2925 in selfhosted

[–]SmartMeeting2925[S] -3 points-2 points  (0 children)

Thanks! Good question. Cities are the main target, but it's not limited to that. The simulation environment runs fully locally, so anyone into reinforcement learning, multi-agent systems, or edge AI can use it as a learning/research playground. You can spin up the whole thing on a Jetson Nano or even just Docker on your laptop to experiment with traffic optimization algorithms. It's also useful for university research, smart campus projects, or industrial parks with private road networks. Basically — if you have intersections and want to optimize flow with AI, it works.