A Novel Approach for Validating Adaptive Cruise Control (ACC) using Two Hardware-in-the-Loop (HIL) Simulation Benches 2019-01-1038

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-cycles 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 target 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 target 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 target vehicle simulation lacks the realistic behavior of a typical target 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 target 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 target vehicle simulation due to the addition of another HIL simulation bench. Two Ford 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 target vehicle, private Controller Area Network (CAN) interfaces between the MABX and the HIL test benches facilitated the transmission of the corresponding target 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 target vehicle over varying speed ranges.