Paper ID: 2311.16900 • Published Nov 28, 2023
Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR
Der-Hau Lee
TL;DR
Get AI-generated summaries with premium
Get AI-generated summaries with premium
The accurate prediction of smooth steering inputs is crucial for automotive
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 novel
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 such that control input stabilization can be achieved in a
computationally simple manner. Two types of automotive lane-keeping experiments
(numerical simulations and experiments involving challenging vision-based
maneuvers) were conducted with a linear system dynamics model to test the
performance of the proposed soft-CILQR algorithm, and its performance was
compared with that of the CILQR algorithm. 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. The results of the vision-based experiments in
which an ego vehicle drove in perturbed TORCS environments with various road
friction settings were consistent with those of the numerical tests. The
proposed soft-CILQR algorithm achieved an average runtime of 2.55 ms and is
thus applicable for real-time autonomous driving scenarios.