10 research outputs found
Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR
The accurate prediction of smooth steering inputs is crucial for autonomous
vehicle applications because control actions with jitter might cause the
vehicle system to become unstable. To address this problem in automobile
lane-keeping control without the use of additional smoothing algorithms, we
developed a soft-constrained iterative linear-quadratic regulator (soft-CILQR)
algorithm by integrating CILQR algorithm and a model predictive control (MPC)
constraint relaxation method. We incorporated slack variables into the state
and control barrier functions of the soft-CILQR solver to soften the
constraints in the optimization process so that stabilizing control inputs can
be calculated in a relatively simple manner. Two types of automotive
lane-keeping experiments were conducted with a linear system dynamics model to
test the performance of the proposed soft-CILQR algorithm and to compare its
performance with that of the CILQR algorithm: numerical simulations and
experiments involving challenging vision-based maneuvers. In the numerical
simulations, the soft-CILQR and CILQR solvers managed to drive the system
toward the reference state asymptotically; however, the soft-CILQR solver
obtained smooth steering input trajectories more easily than did the CILQR
solver under conditions involving additive disturbances. In the experiments
with visual inputs, the soft-CILQR controller outperformed the CILQR controller
in terms of tracking accuracy and steering smoothness during the driving of an
ego vehicle on TORCS.Comment: 11 figures, 10 page
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
Efficient Perception, Planning, and Control Algorithms for Vision-Based Automated Vehicles
Autonomous vehicles have limited computational resources; hence, their
control systems must be efficient. The cost and size of sensors have limited
the development of self-driving cars. To overcome these restrictions, this
study proposes an efficient framework for the operation of vision-based
automatic vehicles; the framework requires only a monocular camera and a few
inexpensive radars. The proposed algorithm comprises a multi-task UNet (MTUNet)
network for extracting image features and constrained iterative linear
quadratic regulator (CILQR) and vision predictive control (VPC) modules for
rapid motion planning and control. MTUNet is designed to simultaneously solve
lane line segmentation, the ego vehicle's heading angle regression, road type
classification, and traffic object detection tasks at approximately 40 FPS
(frames per second) for 228 x 228 pixel RGB input images. The CILQR controllers
then use the MTUNet outputs and radar data as inputs to produce driving
commands for lateral and longitudinal vehicle guidance within only 1 ms. In
particular, the VPC algorithm is included to reduce steering command latency to
below actuator latency to prevent self-driving vehicle performance degradation
during tight turns. The VPC algorithm uses road curvature data from MTUNet to
estimate the correction of the current steering angle at a look-ahead point to
adjust the turning amount. Including the VPC algorithm in a VPC-CILQR
controller on curvy roads leads to higher performance than CILQR alone. Our
experiments demonstrate that the proposed autonomous driving system, which does
not require high-definition maps, could be applied in current autonomous
vehicles.Comment: 10 figures, 13 page