In this paper, we apply a geometrically constrained independent vector analysis (GCIVA) method to an in-car speech enhancement system and confirm its effectiveness in realistic environments. Specifically, we employ GCIVA with the auxiliary function approach and vectorwise coordinate descent (GCAVIVA) to enhance the target speech in in-car communication, where multiple co-occurring speeches are recorded with a triangle microphone array. GCAV-IVA is a recently proposed geometrically constrained blind source separation method, that has been shown to be powerful in directional speech enhancement with a limited number of microphones. Moreover, it is noteworthy for its fast convergence, low computational cost, and non requirement of step-size tuning, which makes it suitable for practical applications. However, the experiments using this method were only conducted using simulated impulse responses (IRs). In this study we investigates GCAV-IVA using measured in-car IRs to simulate more realistic environments. Moreover, we apply GCAV-IVA in a data-adaptive manner. The experimental results revealed that GCAV-IVA significantly outperformed conventional beamforming methods in terms of signal-to-distortion ratio.