Most V2X, ADAS and autonomous driving systems today are based on the precise location and prediction of movement. These systems are computationally complex and depend on precise sensor measurements. This might not be always possible e.g. inaccurate GPS location during cloudy weather conditions. Proposed here is a new approach, the “Backseat Driver”. The “Backseat Driver” is based on heuristics and machine learning concepts for modeling driving guidance. This is very similar to how “humans” drive. The Backseat Driver complements the existing V2X and RADAR based systems by issuing an advisory to the driver. A machine learning approach (Artificial Neural Networks, Expectation-Maximization, Decision trees) is adapted in order to generate advisories. Moreover, with continuous reinforced learning, the predictions become more accurate. In the upcoming days of semi-autonomous driving, a heuristic approach for predicting and issuing advisories could make a significant difference to the driving experience. The Backseat Driver uses historical data and machine learning approach to generate advisories. Timely advisories such as merging traffic, oncoming traffic while taking a turn, busy pedestrian crossing, school/hospital zone could alert the driver to be cautious. The Backseat Driver is a low complexity, low cost system which works even if sensor data is not accurate. Unlike V2X, there is no critical number of installations required for the system to work. In contrast to V2X and ADAS systems which provide alerts only while facing a situation, the Backseat Driver can issue advisories even in advance. The current scope of research shall be limited to issuing an advisory to the driver and not in controlling any vehicle function. In conclusion, the research focus is to provide real time and advance advisory messages to the driver, without dependency on availability of accurate sensor data.