7.5 Assisting Traffic Flow
137
down and to use the full capacity of the freeway, it is important to reduce disruptions
to the flow of vehicles. With this in mind, we created a special kind of adaptive
cruise control (ACC) system, where adjustments are made by a certain proportion
of self-driving cars that are equipped with the ACC system. A traffic control center
is not needed for this. The ACC system includes a radar sensor, which measures
the distance to the car in front and the relative velocity. The measurement data are
then used in real time to accelerate and decelerate the ACC car automatically. Such
radar-based ACC systems already existed before. In contrast to conventional ACC
systems, however, the one developed by us did not merely aim to reduce the burden
of driving. It also increased the stability of the traffic flow and capacity of the road.
Our ACC system did this by taking into account what nearby vehicles were doing,
thereby stimulating a favorable form of self-organization of the overall traffic flow.
This is why we call it a “traffic assistance system” rather than a “driver assistance
system”.
The distributed control approach adopted by the underlying ACC system was
inspired by the way fluids flow. When a garden hose is narrowed, the water simply
flows faster through the bottleneck. Similarly, in order to keep the traffic flow
constant, either the traffic needs to become denser or the vehicles need to drive
faster, or both. The ACC system, which we developed with Volkswagen many years
before people started to talk about Google self-driving cars, imitates the natural interactions and acceleration of driver-controlled vehicles most of the time. But whenever
the traffic flow needs to be increased, the time gap between successive vehicles is
slightly reduced. In addition, our ACC system increases the acceleration of vehicles
exiting a traffic jam in order to reach a high traffic flow and stabilize it. In many
cases, this even allows to dissolve existing traffic jams, as we shall see!
7.6 Creating Favorable Collective Effects
Most other driver assistance systems today operate in a “selfish” way. They are
focused on individual driver comfort rather than on creating better flow conditions
for everyone. Our approach, in contrast, seeks to obtain system-wide benefits through
a self-organized collective effect based on “other-regarding” local interactions. This
is a central feature of what I call “Social Technologies”. Interestingly, even if only
a small proportion of cars (say, 20%) are equipped with our ACC system, this is
expected to support a favorable self-organization of the traffic flow.
3 By reducing
the reaction and response times, the real-time measurement of distances and relative
velocities using radar sensors allows the ACC vehicles to adjust their speeds better
than human drivers can do it. In other words, the ACC system manages to increase
the traffic flow and its stability by improving the way vehicles accelerate and interact
with each other (Fig. 7.3).
3 Kesting et al. [3, 4].
137
down and to use the full capacity of the freeway, it is important to reduce disruptions
to the flow of vehicles. With this in mind, we created a special kind of adaptive
cruise control (ACC) system, where adjustments are made by a certain proportion
of self-driving cars that are equipped with the ACC system. A traffic control center
is not needed for this. The ACC system includes a radar sensor, which measures
the distance to the car in front and the relative velocity. The measurement data are
then used in real time to accelerate and decelerate the ACC car automatically. Such
radar-based ACC systems already existed before. In contrast to conventional ACC
systems, however, the one developed by us did not merely aim to reduce the burden
of driving. It also increased the stability of the traffic flow and capacity of the road.
Our ACC system did this by taking into account what nearby vehicles were doing,
thereby stimulating a favorable form of self-organization of the overall traffic flow.
This is why we call it a “traffic assistance system” rather than a “driver assistance
system”.
The distributed control approach adopted by the underlying ACC system was
inspired by the way fluids flow. When a garden hose is narrowed, the water simply
flows faster through the bottleneck. Similarly, in order to keep the traffic flow
constant, either the traffic needs to become denser or the vehicles need to drive
faster, or both. The ACC system, which we developed with Volkswagen many years
before people started to talk about Google self-driving cars, imitates the natural interactions and acceleration of driver-controlled vehicles most of the time. But whenever
the traffic flow needs to be increased, the time gap between successive vehicles is
slightly reduced. In addition, our ACC system increases the acceleration of vehicles
exiting a traffic jam in order to reach a high traffic flow and stabilize it. In many
cases, this even allows to dissolve existing traffic jams, as we shall see!
7.6 Creating Favorable Collective Effects
Most other driver assistance systems today operate in a “selfish” way. They are
focused on individual driver comfort rather than on creating better flow conditions
for everyone. Our approach, in contrast, seeks to obtain system-wide benefits through
a self-organized collective effect based on “other-regarding” local interactions. This
is a central feature of what I call “Social Technologies”. Interestingly, even if only
a small proportion of cars (say, 20%) are equipped with our ACC system, this is
expected to support a favorable self-organization of the traffic flow.
3 By reducing
the reaction and response times, the real-time measurement of distances and relative
velocities using radar sensors allows the ACC vehicles to adjust their speeds better
than human drivers can do it. In other words, the ACC system manages to increase
the traffic flow and its stability by improving the way vehicles accelerate and interact
with each other (Fig. 7.3).
3 Kesting et al. [3, 4].