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{
    "pk": 65597,
    "title": "Robustness Analysis of Least Squares-based Adaptive Cruise Control in Real-World Scenarios",
    "subtitle": null,
    "abstract": "2025AbstractAs automated driving technologies such as Adaptive Cruise Control (ACC) become commonin the automotive industry, the risk of chain-like crashes and degraded traffic flow increases,especially if string stability and vehicle safety is not ensured. Demonstrating and improving string stability is essential to advancing the design of ACC systems for smoother, safer, and more energy-efficient vehicle platoons. We study how parameter excitability in the regressor matrix influences accuracy and adaptability in ACC systems. When excitation is low, it reduces sensitivity and weakens parameter estimation, making the system less responsive to dynamic conditions. As a result, prediction reliability is compromised, and designing controllers that maintain string stability in actual traffic becomes difficult. We model the ACC system using an ordinary differential equation in which the acceleration of the ego vehicle depends on the spacing, relative velocity, and a constant time progress parameter. Online parameter estimation is performed using a Recursive Least Squares algorithm to capture dynamic changes. To evaluate the role of excitability in the matrix, we analyze the regressor matrix at each update step, quantifying excitability through condition numbers, and convergence of parameters. We\nintroduce diverse driving scenarios that simulate lead and ego vehicle interactions in real-world settings. These driving scenarios are simulated with a lead and ego vehicle velocity modeled in various scenarios: random walk in equilibrium, random walk in non equilibrium, induced curve, and aggressive lead vehicle. Our findings demonstrate that situations with little to no excitation, like random walk equilibrium, had difficulty achieving precise convergence because of a rank deficiency in the regressor matrix. Higher excitation scenarios, such as induced curves, aggressive lead drivers, and random walk (non-equilibrium), on the other hand, showed better convergence and reduced estimation error. In highway scenarios with extended constant speeds, limited excitation was also noted, which resulted in degraded trajectory prediction, parameter drift, and ill-conditioned regressors. In contrast, mixed-driving conditions with periodic excitation showed improved performance, maintaining estimator stability over long periods of time. Overall, these findings show that sustained excitation reflecting realistic traffic variability is necessary for both strong ACC performance and precise online parameter estimation in these driving scenarios.",
    "language": "en",
    "license": {
        "name": "Creative Commons Attribution-NonCommercial  4.0",
        "short_name": "CC BY-NC 4.0",
        "text": "Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nNonCommercial — You may not use the material for commercial purposes.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.",
        "url": "https://creativecommons.org/licenses/by-nc/4.0"
    },
    "keywords": [],
    "section": "Articles",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/7mv1x352",
    "frozenauthors": [
        {
            "first_name": "Axel",
            "middle_name": "",
            "last_name": "Muniz Tello",
            "name_suffix": "",
            "institution": "",
            "department": ""
        }
    ],
    "date_submitted": "2025-12-16T20:24:48Z",
    "date_accepted": "2025-12-16T20:24:48Z",
    "date_published": "2025-01-01T00:00:00Z",
    "render_galley": null,
    "galleys": [
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            "label": "",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/ucm_mwp_ucmurj/article/65597/galley/50226/download/"
        }
    ]
}