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Lane-Keeping Control of Autonomous Vehicles Through a Soft-Constrained Iterative LQR (2311.16900v2)

Published 28 Nov 2023 in cs.CV, cs.RO, cs.SY, and eess.SY

Abstract: 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.

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Summary

  • The paper introduces a soft-CILQR algorithm that integrates slack variables to enable stable lane-keeping in autonomous vehicles.
  • The method was validated through simulations and vision-based driving tests, showing smoother steering and improved performance under noise.
  • The results demonstrate that the approach offers efficient, real-time control, enhancing both stability and ride comfort for autonomous systems.

Introduction to Soft-Constrained Iterative LQR for Autonomous Vehicles

Autonomous vehicles need to execute smooth steering actions for safe and stable driving, especially in challenging scenarios that involve lane-keeping on roads. Standard predictive control methods, which are commonly employed for this task, sometimes result in sharp, undesired inputs due to the presence of various constraints and disturbances, such as actuator limits and environmental noise.

Soft-Constrained Control Strategy

Recent advancements have led to the development of the soft-constrained iterative linear–quadratic regulator (soft-CILQR) algorithm. This method seeks to combine the conventional CILQR approach with a soft-constraint model predictive control (MPC) technique. By introducing slack variables into the optimization process, this new algorithm aims to soften constraints on the state and control inputs, thereby enabling the computation of stabilizing steering outputs more effectively. Importantly, this process involves modifying a vehicle’s lane-keeping control algorithm without the need for any after-the-fact smoothing algorithms or filters.

Comparison with Traditional Methods

The performance of the soft-CILQR algorithm was gauged against the traditional CILQR approach. The evaluations were divided into two parts: numerical simulations and experimental tests using a vision-based driving simulator. Numerical simulations showcased the soft-CILQR's ability to drive the system smoothly towards the reference state despite disturbances. During the vision-based manual experiments, both algorithms demonstrated satisfactory results in lane-keeping tasks on a simulated track. However, soft-CILQR displayed enhanced performance, with less conservative results and a smoother steering trajectory compared to the conventional CILQR, especially when noise was introduced into the system.

Findings and Implications for Self-Driving Technology

The findings indicate that the soft-CILQR can be an effective tool for maintaining the stability and comfort of autonomous vehicles even in the face of external disturbances. Its quick computational time suggests that it is suitable for real-time applications, a crucial aspect for in-the-moment decision-making required in autonomous driving.

In conclusion, the integration of slack variables within a CILQR framework allows for a more robust controller capable of handling the intricate dynamics of autonomous vehicle steering. This approach shows promise for enhancing self-driving technology, offering a path to more reliable and secure autonomous transportation.

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