When driving a vehicle, the driver must allocate adequate attention to the demands of driving in order to be safe. Based on analyses of vehicle data, driving environment data, and videos of the road ahead, a driving task demand prediction model based on real-time road traffic data was established in this study. Assessments of driving task demands allowed for the validation of this model. In addition, intervention strategies were proposed for distractions from in-vehicle secondary tasks at different levels of demand. The analysis showed that at a high driving task demand, the control of all in-vehicle information systems (IVIS), except for the playing of radio or CD, should be warned against or forbidden. Meanwhile, distractions from secondary tasks originated from 91% of the in-vehicle foreign objects, and 67% of the in-vehicle facilities can be avoided by formulating precaution strategies. This study provides methods and technical support for the management of driver distraction precaution.
