New developments in advanced driver assistance systems (ADAS) can help drivers deal with risky driving maneuvers, preventing potential hazard scenarios. A key challenge in these systems is to determine when to intervene. While there are situations where the needs for intervention or feedback is clear (e.g., lane departure), it is often difficult to determine scenarios that deviate from normal driving conditions. These scenarios can appear due to errors by the drivers, presence of pedestrian or bicycles, or maneuvers from other vehicles. We formulate this problem as a driving anomaly detection, where the goal is to automatically identify cases that require intervention. Towards addressing this challenging but important goal, we propose a multimodal system that considers (1) physiological signals from the driver, and (2) vehicle information obtained from the controller area network (CAN) bus sensor. The system relies on conditional generative adversarial networks (GAN) where the models are constrained by the signals previously observed. The difference of the scores in the discriminator between the predicted and actual signals is used as a metric for detecting driving anomalies. We collected and annotated a novel dataset for driving anomaly detection tasks, which is used to validate our proposed models. We present the analysis of the results, and perceptual evaluations which demonstrate the discriminative power of this unsupervised approach for detecting driving anomalies