I have to agree with the previous comments about the lack of detail in the paper.

I do think that traffic control is an important problem to address considering our congested highways, however i did not find the approach considered here too useful for several reasons.

The learning system proposed in the paper was a bit of an overshoot for the problem and was not formally justified in the paper.

Parts of the domain knowledge were dangerous and i believe against the most traffic laws.

The assumptions made by the simulator were far from reality, hence the usefulness of results is highly questionable.

If i'm not mistaken after a few simple calculations one can see that the most severe traffic conditions tested are mildest one in reality. 400 cars in 3 (or 4) lanes over a stretch of 13.3 miles assuming a uniform distribution amounts to roughly 160 meters or 1/10 miles with no cars in front of a given car on average.

While the lane change results show that the policy learned results in more stable driving strategy, the policy was not so successful in achieving the ultimate goal of reducing travel time. From Figure 5a) one can see that the difference between the "errors" of the the learned polity and polite policy are negligible (i.e.: in the case of 400 cars with the learned policy one goes on average 6 miles "bellow" their desired speed, and with the polite policy one goes only 8 miles "bellow" their desired speed. I hardly consider this such a big a gain. You may get to work 1 minute earlier if you are a far-commuter.

Furthermore, I'm not sure how one would like to take directions from a car when people hardly take the advice of other people sitting in the same car.

I think the paper presents an attempt to a smooth transition from manual cars to truly computer guided cars. I truly think that the solution to the ultimate goal of increasing throughput on our highways lies in co-operations between cars.