Connected and autonomous vehicles (CAVs) are expected to save lives by reducing the frequency and severity of traffic accidents and offer additional benefits to the world by reducing travel time and congestion. Challenges exist preventing widespread implementation of CAV technology; notably CAV susceptibility to rear-end collisions, suspected to be due to poor handling of longitudinal distance control in mixed-fleet traffic. This study aimed to determine if it was possible to improve this longitudinal distance control; increasing safety without sacrificing the performance benefits conferred by CAVs. A fixed coupling distance in a custom CAV control protocol was replaced with a linear dynamic model by incorporating a distance and relative velocity modifier. A sensitivity analysis of the modifiers was performed, comparing network performance statistics and conflict and collision rates. Accident frequency and network performance were observed to be more sensitive to the distance modifier than the relative velocity modifier. An increase in the number of CAVs on the road resulted in a faster network, generally at the expense of safety for conventional vehicles (MVs). A select few combinations of velocity and distance modifiers produced networks that outperformed the base case benchmark in both safety and network performance.