Growing congestion in terms of competing technology within, and traffic outside the vehicle has motivated the evolution of advanced safety systems to be context and situation aware by processing multi-sensor information effectively providing timely decisions to assist the driver in driving safely. Towards vehicular and occupant safety, it is important to understand how drivers drive and to identify any variations in their driving performance. One approach to accomplish this is to analyze driving maneuvers. These maneuvers are influenced by the driver's choice and traffic/road conditions, so analyzing these gives an indication of the driving performance. Various framing strategies have been adopted to analyze these continuous temporal information in manageable lengths of data to obtain analysis results as quickly and accurately as possible. Either fixed time window frames or event based frames are amongst the most widely used. However a moving vehicle varies in both time and space with various micro and macro changes in driving patterns based on vehicle speed. In this study, we propose to make use of space/distance traveled by the vehicle rather than time it takes for the vehicle to travel, in the framing strategy to continuously analyze and evaluate vehicle dynamics signals. Initial results using this novel intuitive framing strategy shows an average 11% improvement in maneuver recognition accuracy on a small dataset using CAN-bus signals. Comparable results to time based framing strategy are obtained on naturalistic driving massive corpora, proving that the proposed distance based framing strategy is a computationally efficient alternate framing solution.