Simple scenario to illustrate this: car X enters a narrow bidirectional road which is lined with cars. There is enough room only for one car to pass at a time, safely. Car Y enters from the other end. Several more cars enter behind car Y (we'll call these Y1, Y2, Y3, Y4).

One car must reverse back down the road from the midway point, in order for any cars to pass through.

Car X and its driver cannot see what is behind car Y, but for car Y to reverse it must rely on Y1, Y2, Y3, and Y4 reversing. In order for this to happen, Y4 must first reverse despite not being able to see why it needs to reverse (either through meat or tech sensors).

The optimal solution is that sensor data reviewed in the aggregate by each car leads to a cooperative decision that car X should reverse until it can pull over, and allow the other cars to pass before proceeding.

In an emergency situation it's also possible to envisage scenarios in which shared data analysis and cooperative decision making are optimal. For example, consider cars X and Y now destined for a high speed, head-on collision. The right lane of the road (car X's right, car Y's left) is clear and the left lane (car X's left, car Y's right) is a deep trench. A primitive or non collaborative AI might suggest that both cars swerve to the same direction to avoid collision, which results in a collision of similar magnitude. A better solution than swerving the two cars into each other might be to swerve one into the ditch and one into the road. The optimal solution is likely to swerve only one car and hard stop the other, knowing that the other car is going to move its direction of travel significantly. This can only be done by giving the cars the ability to make decisions collaboratively.