Even the simulation of the city of Braunschweig described in Sect.])—since 1950 more than 100 models have been described solely for the process of following a vehicle driving ahead—can also without further ado be applied as models for autonomous vehicles, albeit with differing parameters for humans and machines as mentioned in Sect. It is thus conceptually quite simple to model mixed traffic and quantify its effects on the transportation system as a whole.However, the parameters with string stability are not perceived as very agreeable by human drivers, so AIC systems generally apply a compromise solution that results in a weak string instability [ are the braking distances of the leading and following vehicles.
One such AIC scenario is highly similar to Use Case #1 “Interstate Pilot Using Driver for Extended Availability”, which in turn (from a traffic-flow standpoint) is a special variant of Use Case #3, “Full Automation Using Driver for Extended Availability”.
This is also the use case that plays the most important role in this chapter, notwithstanding the fact that it is rather irrelevant from the traffic-flow standpoint whether the driver is available or not.
Paper , by contrast, largely ignores questions of traffic flow and traffic control and focuses primarily on the optimal allocation of supply in relation to demand based on the premise that vehicles can be shared.
We can quite rightly conclude at this point that a combination of these approaches, together with a correct description of the share of travelers who would opt for transportation via a robotic “mobility-on-demand” system, allows the best possible appraisal of the potential of autonomous vehicles.
If the leading vehicle has an autonomous emergency braking system that allows deceleration values of up to 12 m/s—leading to a much shorter braking distance.
This can be compensated for to some extent, as the following simulation results also show, because the equations resulting from this approach in the case of strong braking by the leading vehicle can exceed their own deceleration.That is certainly not entirely adequate; and yet driving does work in many cases on the assumption that the other drivers will behave more or less as one does oneself.However, that also means that the approach flowing from this and the following equation can be tricked by “strange” behavior on the part of the leading vehicle.In the following, the focus will be on the process of following a vehicle, which is the most important, but not the only relevant process that determines the development of traffic flow on roads..In multi-lane traffic, the lane in which the vehicle is driving—the lateral coordinate, or distance of the vehicle from the edge of the road—comes in as a variable as well.This underscores the assertion that many driver models and the models for autonomous driving are mathematically very similar.Where they differ will be discussed in greater detail in Sect.A good example of this is the distance to the vehicle ahead expressed in terms of the time gap: an autonomous vehicle can achieve times of 0.3…0.5 s , whereas vehicles driven by humans are legally required to maintain a distance of at least 0.9 s (in Germany).The legal recommendation is actually 2.0 s, but this is seldom maintained except when traffic volumes are low., and is only string stable for a small subset of parameters.String stability is the ability of a chain of vehicles driving behind each other not to succumb to the “slinky effect” and jam up: for instance, when minor braking by the first vehicle in the chain leads to an amplified effect along the chain, in extreme cases actually causing a vehicle in the chain to come to a standstill. To date, this behavior has only been found in very specific situations (see  for an example)—it does not appear to be the normal case.