Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems
This work addresses the challenge of reverse-engineering proprietary ACC systems for automotive industry applications, though it is incremental as it builds on existing neural network and control theory methods.
The paper tackled the problem of learning unknown parameters of proprietary adaptive cruise control (ACC) systems by proposing a physics-inspired neural network (PiNN) that integrates physical laws into the learning process, achieving superior predictive ability in inferring ACC parameters from synthetic and empirical data, and revealing that the stock ACC systems of tested vehicles are not string stable.
This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotive industry. To emulate the core functionality of stock ACC systems, which have proprietary control logic and undisclosed parameters, the constant time-headway policy (CTHP) is adopted. Leveraging the multi-layer artificial neural networks as universal approximators, the developed PiNN serves as a surrogate model for the longitudinal dynamics of ACC-engaged vehicles, efficiently learning the unknown parameters of the CTHP. The PiNNs allow the integration of physical laws directly into the learning process. The ability of the PiNN to infer the unknown ACC parameters is meticulously assessed using both synthetic and high-fidelity empirical data of space-gap and relative velocity involving ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PiNN in learning the unknown design parameters of stock ACC systems from different car manufacturers. The set of ACC model parameters obtained from the PiNN revealed that the stock ACC systems of the considered vehicles in three experimental campaigns are neither $\mathcal{L}_2$ nor $\mathcal{L}_\infty$ string stable.