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Preprints, Working Papers, ...

Identifiability of car-following dynamic

Yanbing Wang 1 Maria Laura Delle Monache 2 Daniel B. Work 1
2 DANCE - Dynamics and Control of Networks
Inria Grenoble - Rhône-Alpes, GIPSA-PAD - GIPSA Pôle Automatique et Diagnostic
Abstract : The advancement of in-vehicle sensors provides abundant datasets to estimate parameters of car-following models that describe driver behaviors. The question of parameter identifiability of such models (i.e., whether it is possible to infer its unknown parameters from the experimental data) is a central system analysis question, and yet still remains open. This article presents both structural and practical parameter identifiability analysis on four common car-following models: (i) the constant-time headway relative-velocity (CTH-RV) model, (ii) the optimal velocity model (OV), (iii) the follow-the-leader model (FTL) and (iv) the intelligent driver model (IDM). The structural identifiability analysis is carried out using a differential geometry approach, which confirms that, in theory, all of the tested car-following systems are structurally locally identifiable, i.e., the parameters can be uniquely inferred under almost all initial condition and admissible inputs by observing the space gap alone. In a practical setting, we propose an optimization-based numerical direct test to determine parameter identifiability given a specific experimental setup (the specific initial conditions and input are known). The direct test conclusively finds distinct parameters under which the CTH-RV and FTL are not identifiable under the given initial condition and input trajectory.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-03171677
Contributor : Maria Laura Delle Monache Connect in order to contact the contributor
Submitted on : Wednesday, March 17, 2021 - 10:15:22 AM
Last modification on : Friday, February 4, 2022 - 3:20:10 AM

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  • HAL Id : hal-03171677, version 1
  • ARXIV : 2103.08652

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Yanbing Wang, Maria Laura Delle Monache, Daniel B. Work. Identifiability of car-following dynamic. 2021. ⟨hal-03171677⟩

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