Page 104 - Dynamic Vision for Perception and Control of Motion

3 Subjects and Subject Classes
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ing tire stiffness and rotational dynamics into account will be shown as contrast for
demonstrating the effects of short maneuver times on dynamic behavior.
Depending on the situation and maneuver intended, different models may be se-
lected. In lateral control, a third-order model is sufficient for smooth and slow con-
trol of lateral position of a vehicle when tire dynamics does not play an essential
role. A fifth-order model taking tire stiffness and rotational dynamics into account
will be shown as contrast for demonstrating the effects of short maneuver times on
dynamic behavior.
Instead of full state feedback, often simple output feedback with a PD- or PID-
controller is sufficient. Taking visual features in 2-D as output variable even works
sometimes (in relatively simple cases like lane following on planar high-speed
roads). Typical tasks solved by feedback control for ground vehicles are given in
the right-hand column of Table 3.3. Controller design for automotive applications
is a well–established field of engineering and will not be detailed here.

3.4.4 Dual Representation Scheme

To gain flexibility for the realization of complex systems and to accommodate the
established methods from both systems engineering (SE) and artificial intelligence
(AI), behaviors are represented in duplicate form: (1) in the way they are imple-
mented on real-time processors for controlling actuators in the real vehicle, and (2)
as abstracted entities for supporting the process of decision making on the mental
representation level, as indicated above (see Figure 3.17).
In the case of simple maneuvers, even approximate analytical solutions of the
dynamic maneuver are available;
they will be discussed in more de- Extended Road running in own lane Artificial
state Decision–making for longitudinal control intelli-
tail in Section 3.4.5 and can be charts gence
Approach Cruise Tran- methods
used twofold: control siti-
(quasi-
1. For computing reference time static) Distance Halt ons
histories of some state variables keeping
or measurement values to be
expected, like heading or lateral Longitudinal guidance Systems
Transit.
position or accelerometer and Control to convoy controller dynamics
Speed
laws
methods
gyro readings at each time, and driving
Controller for
Distance
2. for taking the final boundary controller brake pressure
values of the predicted maneu-
ver as base for maneuver plan-
Figure 3.17. Dual representation of behav-
ning on the higher levels. Just ioral modes: 1. Decision level (dashed), quasi-
transition time and the state
static AI-methods, extended state charts
variables achieved at that time, [Harel 1987] with conditions for transitions
altogether only a few (quasi- between modes. 2. Realization on (embedded,
static) numbers, are sufficient distributed) processors close to the actuators
(symbolic) representations of through feed-forward and feedback control
the process treated, lasting sev- laws [Maurer 2000; Siedersberger 2004]
eral seconds in general.