Learning-Enabled Output–Feedback Design for Lane Change Control: A Comparative Study

Engineering notes

Key topics: autonomous driving system, autonomous driving, lane change, real-world driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract Autonomous lane changing in real-world driving is challenging because sensor noise and practical sensing constraints often limit the availability of reliable full-state measurements. Although many model-based and learning-based control approaches assume access to full-state information, such assumptions may not hold in realistic autonomous driving environments. This paper presents an experimental study comparing control strategies that use a learning-enabled estimator-based output-feedback approach for autonomous lane change maneuvers under realistic state measurement conditions. A small-scale remotely controlled (RC) car platform is used as a scaled experimental testbed to evaluate a model-based model predictive control (MPC) controller, a learning-based state feedback controller, and an estimator-based output-feedback learning controller using real sensor data. The estimator-based controller learns an optimal steering policy online without direct access to full system states. Experimental results show that the estimator-based output feedback demonstrates improved robustness to sensor inaccuracies and external disturbances, smoother lane transitions, and more reliable adaptation in safety-critical scenarios when state measurements are limited or noisy. These results provide experimental evidence that learning-enabled estimator-based output feedback can reduce reliance on expensive sensing configurations while maintaining reliable control performance, supporting its potential practical relevance for autonomous driving systems operating under limited measurement conditions.

Links and sources

Comments