Abstract: In automotive road noise control, feedforward adaptive active noise control systems are typically employed. Currently, the filtered-x least mean square (FxLMS) algorithm and its derivatives are widely adopted due to their simple structure and robustness. Additionally, the frequency-domain algorithms and subband algorithms have also been studied. However, the comprehensive performance of these algorithms in the complex scenario of automotive road noise control has not yet been fully validated and comparatively analyzed. This study conducts a simulation-based comparison of multiple adaptive active control algorithms, leveraging primary noise data and transfer functions from an active headrest system for the passenger seat of an electric vehicle. The evaluation focuses on their convergence speed, noise reduction level, and computational complexity. Results demonstrate that the frequency-domain algorithms can substantially reduce computational load compared to time-domain FxLMS, though the introduced delay may degrade the convergence speed. The subband algorithms exhibit rapid convergence, high noise reduction, and relatively low computational complexity. The frequency-domain block FxLMS algorithms can achieve convergence speed and noise reduction comparable to the subband algorithms. Its computational complexity is higher than that of the frequency-domain algorithms and related to the number of blocks. None of the algorithms converges to the Wiener solution within short timeframes, indicating that in practical implementations, different algorithms should be adopted based on the trade-offs among hardware configuration, system complexity and convergence performance.