Equipment operators play an integral role in the safe and efficient operation of heavy equipment. They observe the environment, understand the situation, and make decisions and actions accordingly. Compared with other types of equipment, operating a crane is more sophisticated and mentally demanding, and thus crane operators are more vulnerable to human errors. Therefore, special considerations to mitigate operator errors should be taken when designing an operator-assistance system for construction cranes. With the goal of improving the operators' situation awareness (SA) of safety risks, this research presents a novel framework and practical system architecture for an operator-assistance system by leveraging real-time motion sensing and 3D modeling of dynamic workspaces. An approach for evaluating operators' SA was proposed to validate the effectiveness of the assistance system in actual lifting operations. Results in a series of field tests indicated that the prototype system improved the operators' SA which resulted in an improved lift performance.