Lateral Control

Lateral control, focusing on steering and path-following in vehicles, is a critical area of research in autonomous driving and robotics. Current efforts concentrate on improving robustness and efficiency, employing techniques like deep learning (including neural networks and graphical neural networks), reinforcement learning, and model predictive control to address challenges such as perception latency and diverse driving conditions. These advancements aim to enhance the safety, comfort, and adaptability of autonomous systems, impacting both the development of self-driving cars and the broader field of robotics control. A key focus is on personalization and handling uncertainty to improve human-machine interaction and system reliability.