# openACC. An open database of car-following experiments to study the properties of commercial ACC systems

arXiv: https://arxiv.org/abs/2004.06342 · HTML: https://mlsanity.com/paper/2004.06342/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [Automatic vehicle trajectory data reconstruction at scale](https://mlsanity.com/paper/2212.07907/) (2022)
- [Car-Following Models: A Multidisciplinary Review](https://mlsanity.com/paper/2304.07143/) (2023)
- [A Generic Stochastic Hybrid Car-following Model Based on Approximate Bayesian Computation](https://mlsanity.com/paper/2312.10042/) (2023)
- [A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving](https://mlsanity.com/paper/2607.07103/) (2026)
- [Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs](https://mlsanity.com/paper/2501.12536/) (2025)
- [Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making](https://mlsanity.com/paper/2210.13683/) (2022)
- [Towards Robust Car Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations](https://mlsanity.com/paper/2402.07139/) (2024)
- [Evidence and quantification of cooperation of driving agents in mixed traffic flow](https://mlsanity.com/paper/2408.07297/) (2024)
- [Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems](https://mlsanity.com/paper/2309.01211/) (2023)
- [Knowledge-data fusion dominated vehicle platoon dynamics modeling and analysis: A physics-encoded deep learning approach](https://mlsanity.com/paper/2502.08658/) (2025)
- [Learn2Drive: A neural network-based framework for socially compliant automated vehicle control](https://mlsanity.com/paper/2510.21736/) (2025)
- [Benchmarking Tesla's Traffic Light and Stop Sign Control: Field Dataset and Behavior Insights](https://mlsanity.com/paper/2512.11802/) (2025)
- [SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons](https://mlsanity.com/paper/2609.06961/) (2026)

## Abstract (arXiv)

Commercial Adaptive Cruise Control (ACC) systems are increasingly available as standard options in modern vehicles. At the same time, still little information is openly available on how these systems actually operate and how different is their behavior, depending on the vehicle manufacturer or model.T o reduce this gap, the present paper summarizes the main features of the openACC, an open-access database of different car-following experiments involving a total of 16 vehicles, 11 of which equipped with state-of-the-art commercial ACC systems. As more test campaigns will be carried out by the authors, OpenACC will evolve accordingly. The activity is performed within the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread. A first preliminary analysis on the properties of the 11 ACC systems is conducted in order to showcase the different research topics that can be studied within this open science initiative.