Adaptive Cruise Control (ACC) is becoming a common feature in modern day vehicles with the advancement of Advanced Driver Assist Systems (ADAS). Simultaneously, Hardware-in-the-Loop (HIL) simulation has emerged as a major component of the automotive product development cycle as it can accelerate product development and validation by supplementing in-vehicle testing. Specifically, HIL simulation has become an integral part of the controls development and validation V-cycle by enabling rapid prototyping of control software for Electronic Control Units (ECUs). Traditionally, ACC algorithms have been validated on a system or subsystem HIL bench with the ACC ECU in the loop such that the HIL bench acts as the host or trailing vehicle with the remote or preceding vehicle usually simulated using as an object that follows a pre-defined motion profile. In this setup, the host vehicle HIL bench generally includes physical components and subsystems or their corresponding simulated representations with varying degrees of fidelity. However, the simulated remote vehicle is typically used as a low fidelity object for which the motion is described only as functions of lateral or longitudinal speed and position. Thus, due to the absence of simulated representations of other physical components and subsystems, the remote vehicle simulation lacks the realistic behavior of a typical remote vehicle which would be used during in-vehicle testing of ACC using two physical vehicles. Therefore, this research proposes a novel approach for validating ACC using HIL simulation benches such that one HIL bench acts as the host vehicle while the other acts as the remote vehicle such that the interaction between the two HIL simulations is more realistic and similar to that observed during in-vehicle testing of ACC with two physical vehicles. This approach would enhance the fidelity of the remote vehicle simulation due to the addition of another HIL simulation bench. Two Ford crossover hybrid powertrain subsystem HIL benches with their corresponding powertrain controllers and actuators were used for this research. For both HIL simulation benches, dSPACE HIL simulators enabled the real-time simulation of other subsystem plants and controllers. A dSPACE Microautobox (MABX) was used for rapid prototyping the ACC algorithm. In lieu of using a simulated sensor for detection of the simulated remote vehicle, a private Controller Area Network (CAN) interface between the MABX and the remote vehicle HIL bench facilitated the transmission of the corresponding remote vehicle HIL information to the MABX for use by the ACC algorithm. Simulations were conducted using this setup to evaluate the performance of the ACC algorithm in maintaining a desired speed and a desired distance to the remote vehicle over varying speed ranges.