MicroSimACC: an open database for field experiments on the potential capacity impact of commercial Adaptive Cruise Control (ACC)

Abstract

Commercial availability of vehicle automation has become mainstream. Most of today’s new vehicles can perform longitudinal car following autonomously via Adaptive Cruise Control (ACC). Field experiments demonstrate that today’s commercially available ACC vehicles provide similar headways and capacities as human-driven vehicles on freeways under steady-state and free-flow conditions. However, field tests also demonstrated that the design of today’s commercially available ACC vehicles can lead to further capacity reduction when operating in non-steady-state conditions where queues are present and speeds frequently fluctuate. These experiments generated MicroSimACC, a comprehensive set of field data that encompasses full speed range car following with interruptions from lane change manoeuvres. This will benefit the research community by providing benchmark data for developing models to be integrated into microscopic simulations for more prospective analyses and planning.

KEYWORDS:

• Adaptive Cruise Control (ACC)
• field experiments
• capacity
• car following

Acknowledgements

Mingyuan Yang: Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualisation. Pablo Chon-Kan Munoz: Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualisation. Servet Lapardhaja: Methodology, Software, Validation, Investigation, Data Curation, Writing – Review & Editing, Visualisation. Yaobang Gong: Methodology, Software, Validation, Investigation, Data Curation, Visualisation. Md. Ashraful Imran: Validation, Formal analysis, Investigation, Data Curation, Visualisation. Md. Tausif Murshed: Methodology, Validation, Investigation, Data Curation, Visualisation. Kemal Yagantekin: Methodology, Software, Validation, Investigation, Data Curation. Md Mahede Hasan Khan: Methodology, Validation, Formal analysis, Data Curation. Xingan (David) Kan: Conceptualisation, Methodology, Validation, Investigation, Resources, Writing – Original Draft, Writing – Review & Editing, Supervision, Project administration, Funding acquisition. Choungryeol Lee: Methodology, Software, Validation, Investigation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The car-following data collected can be found at https://github.com/microSIM-ACC/ICE.

Notes

1 Building a comprehensive dataset for a large variety of vehicles is not feasible given time and budget constraints. Besides, similar ACC car following behaviours for internal combustion engine (ICE) vehicle from other manufacturers can be expected because recent field studies have shown that different commercial ACC systems behave similarly (Makridis, Mattas, and Ciuffo 2019). In this study, the chosen test vehicle (i.e. Toyota Corolla) is the best-selling car in the world and will represent a large portion of ACC equipped vehicles on the road today.

2 The manufacturer provides higher frequency for the proprietary data but that cannot be decoded and converted to formats usable to the researchers unless granted exclusive access by the manufacturers. This is practically not feasible. The OBD-II data can only be decoded by the public at such frequency. At the time of the experiment, GPS technologies were not convenient and accurate enough to provide far more superior data. In our follow-up study for EVs with ACC, we adopted a high-frequency and high-resolution GPS device called RaceBox (Lapardhaja et al. 2023).

3 We cannot rely on the ‘catch-up’ process to recover the initial steady state headway and capacity in the real-world, since it takes a long time and could be easily interrupted. Besides, it could induce additional speed fluctuations upstream, and eventually exchange a smaller gap downstream for a longer gap upstream. The purpose of this test is to present a complete trajectory for developing ACC car-following models in the future.

4 This study did not systematically test every cut-in position because it is extremely difficult for the drivers to accurately measure/estimate the position during a short lane-change period (usually lasts for less than 3 s). Note that some short cut-in spacings/positions are simply not feasible in the real world. It is also dangerous especially when the lane-change speed is relatively low compared to the free-flow speed and the gap is short. Instead, this study allowed the lane changers to choose the cut-in positions on their own, meaning that the distribution of the position should be close to that in the real world considering the number of repeated trials. Therefore, the lane-change impact we investigated in this study can be viewed as the impact at the average cut-in position that drivers take in the real-world.

Additional information

Funding

This paper and the work described were sponsored by the National Science Foundation (NSF) under the Civil Infrastructure Systems (CIS) program [award number 2301446].