Page 105 - Dynamic Vision for Perception and Control of Motion

3.4 Behavioral Capabilities for Locomotion 89

3.4.4.1 Representation for Supporting the Process of Decision-Making

Point 2 constitutes a sound grounding of linguistic situation aspects. For example,
the symbolic statement: The subject is performing a lane change (lateral offset of
one lane width) is sufficiently precise for decision-making if the percentage of the
maneuver already performed and vehicle speed are known. With respect to the end
of this maneuver, two more linguistic aspects can be predicted: The subject will
have the same heading direction as at the start of the maneuver and the tangential
velocity vector will be at the center of the neighboring lane being changed to.
In more complicated situations without analytical solutions available, today's
computing power allows numerical integration of the corresponding equations over
the entire maneuver time within a fraction of a video cycle and the use of the nu-
merical results in a way similar to analytical solutions.
Thus, a general procedure for combining control engineering and AI methods
may be incorporated. Only the generic nominal control time histories u ff(·) and
feedback control laws guaranteeing stability and sufficient performance for this
specific maneuver have to be stored in a knowledge base for generating these “be-
havioral competencies”. Beside dynamical models, given by Equation 3.6 and 3.8
for each generic maneuver element, the following items have to be stored:
1. The situations when it is applied (started and ended), and
2. the feed-forward control time histories u ff(·); together with the dynamic models.
This includes the capability of generating reference trajectories (commanded
state time histories) when feedback control is applied in addition to deal with
unpredictable perturbations.
All these maneuvers can be performed in different fashions characterized by some
parameters such as total maneuver time, maximum acceleration or deceleration al-
lowed, rate of control actuation, etc. For example, lane change may either be done
in 2, 6, or in 10 seconds at a given speed. The characteristics of a lane change ma-
neuver will differ profoundly for the speed range of modern vehicles when all real-
world dynamic effects are taken into account. Therefore, the concept of maneuvers
may be quite involved from the point of view of systems dynamics. Maneuver time
need not be identical with the time of control input; it is rather defined as the time
until all state variables settle down to their (quasi-) steady values. These real-world
effects will be discussed in Section 3.4.5; they have to be part of the knowledge
base and have to be taken into account during decision-making. Otherwise, the dis-
crepancies between internal models and real-world processes may lead to serious
problems.
It also has to be ensured that the models for prediction and decision-making on
the abstract (AI-) level are equivalent – with respect to their outcome – to those
underlying the implementation algorithms on the systems engineering level. Figure
3.17 shows a visualization of the two levels for behavior decision and implementa-
tion [Maurer 2000, Siedersberger 2004].

3.4.4.2 Implementation for Control of Actuator Hardware
In modern vehicles with specific digital microprocessors for controlling the actua-
tors (qualified for automotive environments), there will be no direct access to ac-