Modern vehicles have incorporated numerous safety-focused Advanced Driver Assistance Systems (ADAS) in the last decade including smart cruise control and object avoidance. In this paper, we aim to go beyond using ADAS for safety, and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy. We combine ADAS sensor data with a previously developed prediction model, dynamic programming optimal energy management control, and a validated model of a 2010 Toyota Prius to explore fuel economy. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real world city and highway drive cycles in Denver Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various known ADAS algorithms are combined, modified, and compared to the ground truth results. Lastly, four vehicle control strategies are evaluated for fuel economy: the existing vehicle control, actual ADAS detection for prediction and optimal energy management, ground truth ADAS detection for prediction and optimal energy management, and 100% accurate prediction and optimal energy management. Results show that the defined ADAS scope and algorithms provide close correlation with ADAS ground truth and can enable fuel economy improvements when used with prediction based optimal energy management. Our proposed approach can leverage existing ADAS technology on modern vehicles to realize prediction based optimal energy management fuel economy improvements with minor modifications.