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Yes, this is for self-driving cars.

I think you under-estimate the numbers of cars on the road.

I don't remember the exact throughput used for the video, but I think it was around 5500. On a equivalent road the minimum distance between two cars is about 2 seconds. That gives a throughput of 1800 cars per road per lane (3600/2). For two lanes (resembling the one used in the video) this would give a absolute maximum throughput of 3600 roads per hour.

Which means that our system, with about 5000 cars per hour, give a much higher throughput than the maximal on a standard highway.

All the details is in the thesis (link in the url, or https://highwayflocking.github.io/Flocking_for_Road_Traffic_...).

could you easily simulate human speed control, with gentle variance and effective repulsion between vehicles? or overload the road and see how it breaks down.

Yep, in our thesis we have tried with throughputs from 2000 to about 15000, and with different types of load. We assume that all vehicles are automatic, so no human speed control.

How feasible would it be to simulate non-automatic vehicles with the models you've provided? My hypothesis is that humans, too, could implement the proposed steering behaviors (albeit likely with some degree of imperfection) and thus could feasibly implement some or all of the proposed flocking strategy, but I wouldn't know where to begin with testing things like blind spots and slower reaction times.

At the very least, I reckon the "yield to higher-priority traffic", "maintain distance from other vehicles", "maintain distance from road boundaries", and "drive forward" behaviors to be (relatively) easy for human drivers to implement, seeing as they do these things already (albeit with lanes as visual reference points), and do so even better with modern driving assistance technologies like blind spot indicators. Dynamic adjustment to asymmetric load would be harder, but I suspect even this could be feasible.

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