20 research outputs found
Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving
Adverse weather conditions and occlusions in urban environments result in
impaired perception. The uncertainties are handled in different modules of an
automated vehicle, ranging from sensor level over situation prediction until
motion planning. This paper focuses on motion planning given an uncertain
environment model with occlusions. We present a method to remain collision free
for the worst-case evolution of the given scene. We define criteria that
measure the available margins to a collision while considering visibility and
interactions, and consequently integrate conditions that apply these criteria
into an optimization-based motion planner. We show the generality of our method
by validating it in several distinct urban scenarios
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
In the context of autonomous driving, the iterative linear quadratic
regulator (iLQR) is known to be an efficient approach to deal with the
nonlinear vehicle models in motion planning problems. Particularly, the
constrained iLQR algorithm has shown noteworthy advantageous outcomes of
computation efficiency in achieving motion planning tasks under general
constraints of different types. However, the constrained iLQR methodology
requires a feasible trajectory at the first iteration as a prerequisite. Also,
the methodology leaves open the possibility for incorporation of fast,
efficient, and effective optimization methods (i.e., fast-solvers) to further
speed up the optimization process such that the requirements of real-time
implementation can be successfully fulfilled. In this paper, a well-defined and
commonly-encountered motion planning problem is formulated under nonlinear
vehicle dynamics and various constraints, and an alternating direction method
of multipliers (ADMM) is developed to determine the optimal control actions.
With this development, the approach is able to circumvent the feasibility
requirement of the trajectory at the first iteration. An illustrative example
of motion planning in autonomous vehicles is then investigated with different
driving scenarios taken into consideration. As clearly observed from the
simulation results, the significance of this work in terms of obstacle
avoidance is demonstrated. Furthermore, a noteworthy achievement of high
computation efficiency is attained; and as a result, real-time computation and
implementation can be realized through this framework, and thus it provides
additional safety to the on-road driving tasks.Comment: 9 pages, 8 figure