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      A review of research on tire burst and vehicle stability control

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          Abstract

          Tire burst is an accidental occurrence that poses a serious threat to the driving stability and road safety of vehicles. Therefore, it is of great practical significance to investigate early warning systems for tire burst and develop stability and safety control measures after burst incidents. The development of an accurate model that can effectively represent the impact of tire burst on vehicle dynamics is crucial for the design of control systems and the development of stability control strategies. Most of the existing research on tire burst models is based on static tire tests, the effectiveness of these models still needs to be further verified. The main approach to studying the impact of burst tires on vehicle performance is to embed a burst tire model into a vehicle dynamics model. Understanding the impact of tire burst on vehicle performance is essential for identifying burst incidents and developing stability control strategies. The research on burst identification primarily focuses on early warning systems and estimating vehicle state parameters after burst incidents, while the current research on stability control strategies focuses on enabling vehicles to continue running safely after burst incidents through braking, active steering, and collaborative control. Currently, there is no comprehensive review of research on vehicle tire burst stability control. Therefore, this paper primarily reviews five aspects: (a) the causes and prevention of tire burst, (b) the impact of tire burst on vehicle performance, (c) burst identification, (d) stability control strategies for burst incidents, and (e) future prospects for tire burst research.

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          Neural network vehicle models for high-performance automated driving

          Automated vehicles navigate through their environment by first planning and subsequently following a safe trajectory. To prove safer than human beings, they must ultimately perform these tasks as well or better than human drivers across a broad range of conditions and in critical situations. We show that a feedforward-feedback control structure incorporating a simple physics-based model can be used to track a path up to the friction limits of the vehicle with performance comparable with a champion amateur race car driver. The key is having the appropriate model. Although physics-based models are useful in their transparency and intuition, they require explicit characterization around a single operating point and fail to make use of the wealth of vehicle data generated by autonomous vehicles. To circumvent these limitations, we propose a neural network structure using a sequence of past states and inputs motivated by the physical model. The neural network achieved better performance than the physical model when implemented in the same feedforward-feedback control architecture on an experimental vehicle. More notably, when trained on a combination of data from dry roads and snow, the model was able to make appropriate predictions for the road surface on which the vehicle was traveling without the need for explicit road friction estimation. These findings suggest that the network structure merits further investigation as the basis for model-based control of automated vehicles over their full operating range.
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            Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability

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                Author and article information

                Journal
                Sci Prog
                Sci Prog
                SCI
                spsci
                Science Progress
                SAGE Publications (Sage UK: London, England )
                0036-8504
                2047-7163
                17 September 2024
                Jul-Sep 2024
                : 107
                : 3
                : 00368504241272478
                Affiliations
                [1 ]Ringgold 649946, universitySchool of Vehicle and Mobility, Tsinghua University; , Beijing, China
                [2 ]Automotive Product Strategy & New Technology Research, Ringgold 666553, universityBYD Auto Industry Company Limited; , Shen Zhen, China
                Author notes
                [*]Jun Li, School of Vehicle and Mobility, Tsinghua University, Beijing 100000, China. Email: lijun1958@ 123456tsinghua.edu.cn
                Author information
                https://orcid.org/0000-0002-0437-5112
                https://orcid.org/0000-0001-5099-8401
                Article
                10.1177_00368504241272478
                10.1177/00368504241272478
                11418262
                39285777
                c81d093e-72d4-40e2-a886-2b1c1bdccf18
                © The Author(s) 2024

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: Research on the corresponding mechanism of mechanical-like load and its safety reliability of autonomous vehicle;
                Award ID: 52172388
                Categories
                Engineering & Technology
                Custom metadata
                ts19
                July-September 2024

                tire burst,stability control method,parameter estimation,tire pressure monitoring system,vehicle performance

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