Driver's distraction, inattention, sleepiness, stress, etc. are identified as causal factors of vehicle crashes and accidents. Today, we know that physiological signals are convenient and reliable measures of drivers impairments. Heterogeneous sensors are generating vast amount of signals, which need to be handled and analyzed in a big data scenario. Here, we propose a big data analytics approach for driver state monitoring using heterogeneous data that are coming from multiple sources, i.e., physiological signals along with vehicular data and contextual information. These data are processed and analyzed to aware impaired vehicle drivers.