A model-based design of an onboard stereo vision system: obstacle motion estimation for cooperative automated vehicles

Kemsaram, Narsimlu; Das, Anweshan; Dubbelman, Gijs

doi:10.1007/s42452-022-05078-w

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Tian et al 10 proposed a method to realize a comprehensive evaluation of wheel dynamics state by information fusion. Based on object-oriented analysis, Kemsaram et al 11 presented a design model of a stereo vision system for cooperative automated vehicles. Zhang et al 12 proposed a distributed bearing-based formation control scheme to extend the application domain.…”

Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

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Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.

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Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

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“…Mo et al proposed a framework of vehicle-infrastructure cooperative perception for the cooperative automated driving system to overcome the technical bottlenecks and limitations of autonomous vehicles on the information perception [3]. Kemsaram et al presented a design model of a stereo vision system for cooperative automated vehicles based on the object-oriented analysis and design methodologies [4]. Yin et al proposed to approximate the optimal filter gain by considering the effect factors within the infinite time horizon, on the basis of estimation-control duality [5].…”

Section: Introductionmentioning

confidence: 99%

Vehicle state and parameter estimation based on improved extend Kalman filter

Liu,

Cui,

Peng

2023

J. meas. eng.

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In order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods.

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