@mastersthesis {pub3894,
	title = {Predictive trajectory planning for lag compensation in a longitudinal and lateral vehicle control system},
	author = {Raphael Wenzel},
	year = {2018},
	month = {December},
	abstract = {This work is concerned with the implementation of a Motion Planning approach to an existing proto-
type vehicle at HRI Europe. The platform vehicle uses a special actuator architecture which has to be
accounted for in the motion planning task, since it introduces additional delays in the actuation path.
Previous projects found the existing dead times and delays inherent to the autonomous vehicle{\textquoteright}s ac-
tuation system to be responsible for the insufficient behavior. This work proposes a motion planning
system for highway scenarios which is capable of encountering the problems at hand by extending
state of the art approaches. A system identification on the real vehicle{\textquoteright}s longitudinal and lateral actu-
ation path is conducted to identify the influence of various delays on the actuation and the vehicle{\textquoteright}s
dynamics. Based on the results of this identification, a system design is proposed.
The system is therefore divided into two main components. The Trajectory Planning provides a trans-
lation of the given Motion Primitives into a comfortable and collision-free trajectory while introduc-
ing countermeasures to the system{\textquoteright}s dead time by computing the trajectory for a predicted scenario.
This prediction can introduce negative dead time to the overall system and therefore counters the
negative effects described. The trajectories are computed by leveraging optimal control theory and
Indirect Methods of optimization. By striving to make this approach applicable to any trajectory plan-
ning approach, a coordinate transformation is proposed which transforms any given highway situation
into a straightened road. A number of longitudinal and lateral Motion primitives are proposed to offer
a higher-level behavioral intelligence a universal interface. The resulting, optimal trajectory is jerk-
optimized, temporal consistent, collision-free and constrained in its dynamics.
The subsequent Trajectory Tracking Control translates the reference trajectory given by the planning
instance into actuator commands for the steering wheel servo motor. It also inherits the role of a
closed loop function, minimizing the offset of the actual vehicle{\textquoteright}s motion to the reference trajectory
given by the planning instance. It is also responsible for providing a stable vehicle guidance by taking
the various delays into account which are introduced in the actuation chain. This is accomplished by
utilizing Model Predictive Control functionality. The established model provides a combination of
both the vehicle movement relative to the reference trajectory with a model for the actuator dynamics
of the vehicle obtained in the identification. This enables the MPC to correct small deviations from
the reference according to a fully parametrizable cost function while accounting for constraints on
each defined state.},
	publisher = {HRI-EU},
	booktitle = {HRI-EU}
}