Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving

Wei, Hong-Jian; Hu, Fuzhi; Zhao, Baigan; Guo, Zhiyang; Zhang, Rui

doi:10.3390/rs13091828

Cited by 12 publications

(8 citation statements)

References 36 publications

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“…The framework is divided into two parts: feature extraction and data association on the temporal window. In the first stage, our previous work [33] is utilized to accurately segment the objects in the detected bounding boxes by YOLOX [21]. Then, we design an object appearance feature extraction network based on metric learning to obtain discriminative appearance features and apply a motion model to estimate the position information of each trajectory in the trajectories set at the current frame.…”

Section: Methodsmentioning

confidence: 99%

Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving

Wei,

Huang,

Zhang

et al. 2023

WEVJ

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Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches.

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Section: Methodsmentioning

confidence: 99%

Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving

Wei,

Huang,

Zhang

et al. 2023

WEVJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…The commonly used vehicle state parameter estimation methods include Kalman filter (KF) and its improved algorithms, [7][8][9][10][11][12][13][14][15][16][17] neural network estimation algorithms, [18][19][20] and other related estimation algorithms. [21][22][23][24][25][26][27] However, single estimation algorithms have their own limitations, such as the uncertainty of mathematical model parameters, noise parameters, and the coverage of training samples, which will affect the estimation results and may lead to the sudden divergence of estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

Zha

Liu

et al. 2022

International Journal of Distributed Sensor Networks

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To improve the reliability of vehicle state parameter estimation, a vehicle state fusion estimation method based on dichotomy is proposed. An extended Kalman filter algorithm is designed based on the vehicle 3 degrees of freedom dynamic model. Meanwhile, considering the influence of dynamic model and sensor noise and its coefficient selection on the estimation results, a radial basis function neural network estimation algorithm is designed. To further improve the reliability of the estimation algorithm, a method of estimation algorithm fusion is proposed based on the idea of mutual compensation between model- and data-driven estimation algorithms. The weights of the estimation results of different algorithms are assigned through the dichotomy. The redundancy and fusion of estimation algorithms can improve estimation performance. The effectiveness of the fusion method is verified by the co-simulation of MATLAB/Simulink and CarSim, and the real vehicle test. The results show that the change trend of the estimation result is consistent with the actual state parameters change trend, and the estimation accuracy after algorithm fusion is significantly improved compared to a single extended Kalman filter or radial basis function.

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“…Under the assumption that the prior position and orientation are known, the Kalman filter may efficiently anticipate obstacle positions. (Wei, H., et al, 2021) proposed a method for estimating motion state based on region-level instance segmentation and the extended Kalman filter (EKF). To create optimum motion parameters, the EKF model takes into account ego-motion and integrates it along with optical flow and disparity.…”

Section: Introductionmentioning

confidence: 99%

Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

Hassan

Saleem

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Navigation in dynamic environments for mobile robots is a difficult problem as it involves estimating the path of moving obstacles. The measured data usually contains a bias and noise in addition to its true value. Based on a stacked denoising autoencoder (SDAE), the enhanced Kalman filter developed in this paper can estimate the obstacle position from any type of noisy input. The extended Kalman filter's ability to predict an error-free path is impacted by the measurement noise covariance matrix employed. The SDAE is a neural network topology based on deep learning that can be used to determine the optimum covariance matrix. Both Adam and stochastic gradient learning algorithms are used to train the neural network. The robot's path is re-planned based on the predicted obstacle path to ensure safe navigation. MATLAB-based numerical simulations are used to demonstrate the utility and superiority of the proposed method over the traditional Kalman filter and Particle filter methodologies. The simulation results show that in the presence of any sort of noise, the proposed technique is exceptionally durable and reliable. The simulation findings also reveal that when it comes to denoising the measured data, the stacked denoising autoencoder with Adam optimizer is more efficient than the stochastic approach. The performance of the developed algorithm is validated in MATLAB simulated environments, and it can be extended for navigation tasks. In terms of computation time and robustness in closely spaced obstacles, simulation experiments demonstrated that the path planning using the proposed algorithm outperforms the hybrid A star, artificial potential field, and decision algorithms.

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Motion Estimation Using Region-Level Segmentation and Extended Kalman Filter for Autonomous Driving

Cited by 12 publications

References 36 publications

Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving

Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

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