Vehicle state estimation based on Kalman filters

Bersani, Mattia; Vignati, Michele; Mentasti, Simone; Arrigoni, Stefano; Cheli, Federico

doi:10.23919/eeta.2019.8804527

Cited by 22 publications

(12 citation statements)

References 17 publications

(25 reference statements)

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“…Authors in [58] compared performances between EKF and UKF for a similar estimation procedure. Results assess that UKF provides more accurate results, ensuring fast computational time.…”

Section: State Estimationmentioning

confidence: 99%

An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

Bersani

Mentasti

Dahal

et al. 2021

Robotics and Autonomous Systems

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Authors in [58] compared performances between EKF and UKF for a similar estimation procedure. Results assess that UKF provides more accurate results, ensuring fast computational time.…”

Section: State Estimationmentioning

confidence: 99%

An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

Bersani

Mentasti

Dahal

et al. 2021

Robotics and Autonomous Systems

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“…Unscented Kalman filtering (UKF) overcomes the shortcomings of traditional Kalman filtering and extended Kalman filtering [14]. The sampling points are used to approximate the state distribution without calculating the Jacobian matrix, and the calculation accuracy is higher.…”

Section: Design Of Unscented Kalman Filtering Algorithmmentioning

confidence: 99%

Observation of Dynamic State Parameters and Yaw Stability Control of Four-Wheel-Independent-Drive EV

Zhang

Chang

Zhang

et al. 2021

WEVJ

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Vehicle yaw stability control is an important part of the active safety of electric vehicles. In order to realize the yaw stability control of vehicles, this paper takes 4-WID electric vehicles as the research object, studies the nonlinear estimation of the state parameters of the lateral stability dynamic system and the yaw stability control strategy. The vehicle state parameter estimation strategy, based on the unscented Kalman filter (UKF) algorithm and the model predictive control algorithm, are designed to control the vehicle yaw stability, which realizes the safe and stable driving of the vehicle. Through CarSim–Simulink joint simulation and hardware-in-the-loop (HIL) experiments based on MicroAutoBox, the effectiveness and real-time performance of the designed control strategy are fully verified, which accelerate the development process of the vehicle controller, and realizes the safe and stable driving of the vehicle.

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“…This task is implemented using MATLAB programming language on the x86 laptop. Several techniques have been evaluated by the authors as reported in [14], and finally, a UKF algorithm was chosen. In particular, the solution adopted makes use of the road reference frame both for obstacle tracking and ego vehicle state estimation instead of a more traditional Cartesian reference frame on which sensors provide data.…”

Section: Software Architecturementioning

confidence: 99%

Design of a prototypical platform for autonomous and connected vehicles

Arrigoni,

Mentasti,

Cheli

et al. 2021

Preprint

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Self-driving technology is expected to revolutionize different sectors and is seen as the natural evolution of road vehicles. In the last years, real-world validation of designed and virtually tested solutions is growing in importance since simulated environments will never fully replicate all the aspects that can affect results in the real world. To this end, this paper presents our prototype platform for experimental research on connected and autonomous driving projects. In detail, the paper presents the overall architecture of the vehicle focusing both on mechanical aspects related to remote actuation and sensors set-up and software aspects by means of a comprehensive description of the main algorithms required for autonomous driving as ego-localization, environment perception, motion planning, and actuation. Finally, experimental tests conducted in an urban-like environment are reported to validate and assess the performances of the overall system.

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Vehicle state estimation based on Kalman filters

Cited by 22 publications

References 17 publications

An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

Observation of Dynamic State Parameters and Yaw Stability Control of Four-Wheel-Independent-Drive EV

Design of a prototypical platform for autonomous and connected vehicles

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