Undergraduate Researcher | UC Merced
Authors: Axel Muñiz Tello, Ayush Pandey
As Adaptive Cruise Control (ACC) systems become more common, maintaining string stability is critical for safety and traffic efficiency. This study examines how parameter excitability in the regressor matrix influences the accuracy and adaptability of ACC systems. Using a Recursive Least Squares algorithm for online parameter estimation, we found that low excitation reduces sensitivity and leads to poor parameter convergence, especially during steady-speed driving. High-excitation conditions such as curves or aggressive maneuvers improve estimator performance and system responsiveness. Simulations across multiple driving conditions show that realistic, variable excitation produces more stable and reliable results. These findings highlight that traffic variability is not noise but a key ingredient for developing smarter, safer, and more adaptable automated driving systems.