Multiple-input multiple-output arrays in frequency modulated continuous wave (FMCW) radars provide the angular resolution required to isolate objects in complex automotive scenes. However, the demodulation of individual antenna contributions increases the chirp repetition interval, which reduces the maximum unambiguously measurable velocity. Objects moving faster than this velocity are then incorrectly folded in the range-Doppler maps, leading to critical inaccuracies in the advanced driver assistance systems. Here, this problem is addressed from the binary phase modulation (BPM) perspective, which is a scheme particularly attractive due to its high signal-to-noise ratio, and whose intrinsic phase alterations prevent traditional disambiguation techniques from being reliable. The authors present a detailed analysis on the design, implementation, and validation of Doppler disambiguation techniques for BPM systems. The presented benchmark includes a variety of algorithms involving the Chinese remainder theorem, density-based spatial clustering of applications with noise (DBSCAN), hypothetical phase compensation, and an intuitive range-based approach. The techniques are extensively validated using synthetic data generated with MATLAB, and real data collected with a 77-GHz FMCW radar. The results show the best set of trade-offs for the enhanced DBSCAN (EDBSCAN) method in terms of robustness, computational overhead, velocity span, and disambiguation rate.