# Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems

arXiv: https://arxiv.org/abs/2309.01211 · HTML: https://mlsanity.com/paper/2309.01211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [DeepXDE: A deep learning library for solving differential equations](https://mlsanity.com/paper/1907.04502/) (2019)
- [Are commercially implemented adaptive cruise control systems string stable?](https://mlsanity.com/paper/1905.02108/) (2019)
- [openACC. An open database of car-following experiments to study the properties of commercial ACC systems](https://mlsanity.com/paper/2004.06342/) (2020)
- [Model based string stability of adaptive cruise control systems using field data](https://mlsanity.com/paper/1902.04983/) (2019)
- [String stability and a delay-based spacing policy for vehicle platoons subject to disturbances](https://mlsanity.com/paper/1702.01031/) (2017)
- [Estimating adaptive cruise control model parameters from on-board radar units](https://mlsanity.com/paper/1911.06454/) (2019)
- [The Unscented Kalman Filter for Nonlinear Parameter Identification of Adaptive Cruise Control Systems](https://mlsanity.com/paper/2306.03458/) (2023)
- [Energy-based Assessment and Driving Behavior of ACC Systems and Humans Inside Platoons](https://mlsanity.com/paper/2306.04476/) (2023)

## Abstract (arXiv)

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 L2 nor L_∞ string stable.