Millimeter wave (mmWave) is a practical solution to provide high data rate for the vehicle-to-everything (V2X) communications. This enables the future autonomous vehicles to exchange big data with the base stations (BSs) such as the velocity and the location to improve the awareness of the advanced driving assistance system (ADAS). In this context, we consider a single-cell multiuser doubly-selective system wherein the BS simultaneously serves multiple vehicles. To accomplish this requirement, the BS is implemented in hybrid architecture to support multiple spatial streams while the vehicles have analog-only structures. In this work, we develop a low-complexity hybrid precoding algorithm wherein the design of the hybrid precoder at the BS and the analog combiner at the vehicles require small training and feedback overhead. We propose a two-stage hybrid precoding algorithm wherein the first stage designs the analog beamformers as in single user scenario while the second stage designs the multiuser digital precoder at the BS. In the second stage, we derive closed-form digital precoders such as Maximum Ratio Transmission (MRT), Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) as a first variant while we propose iterative digital precoder as a second variant. The design of the digital precoders for the two variants requires the limited feedback sent from the vehicles to BS. We refer to the random vector quantization (RVQ) and the beamsteering codebooks to quantize the feedbacks for variants I and II, respectively, since the perfect feedback requires long overhead and large training. We evaluate the rate loss incurred by the quantization of the digital and analog codebooks against the perfect channel state information at the transmitter (CSIT).