Lateral State Estimation of Preceding Target Vehicle Based on Multiple Neural Network Ensemble

Li, Chengwei; Zhou, Zhisong; Wu, Jia; Jin, Wenqiang; Yin, Chengliang

doi:10.1109/ivs.2019.8814232

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Recently, neural network techniques have been vigorously applied to estimating various vehicle states [14][15][16][17][18][19][20][21]. Neural network structures can overcome the limitations of empirical methods because they can identify data characteristics that a human observer cannot detect [22].…”

Section: Introductionmentioning

confidence: 99%

“…Another study employed a feedforward neural network with a fully connected model to estimate road grade and vehicle mass [15]. Depending on the purpose, neural network models have been adopted in various forms [16][17][18]. Time variant data were processed by a time-delayed neural network in a sideslip angle estimation [16].…”

Section: Introductionmentioning

confidence: 99%

“…An integrated time-series model based on a multivariate deep recurrent neural network with long short-term memory was demonstrated to estimate vehicle brake pressure [17]. Another effort attempted to solve the lateral state estimation problem of a preceding target vehicle using multiple neural networks, consisting of a nonlinear autoregressive exogenous model net, feedforward net, and Elman net [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles

Kim

Yoon

2020

IEEE Access

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This study employs a dual deep neural network (D-DNN) to accurately estimate the absolute longitudinal speed of a vehicle. Accuracy in speed estimation is crucial for vehicle safety, because longitudinal speed is a common parameter employed as a state variable in active safety systems such as antilock braking system and traction control system. In this study, DNNs are applied to determine the gain of an adaptive filter to estimate vehicle speed. The used data consists of longitudinal acceleration, wheel speed, filter gain, and estimated vehicle speed. The data generated from Carsim software are collected and preprocessed using a Simulink model. To acquire data with numerous wheel slip patterns, various acceleration and deceleration conditions are applied to four different road conditions. Though, it is challenging to achieve a single DNN model that is optimally cope with the various driving situations. Thus, we adopt two DNN models that were individually trained in low and high acceleration regions. The dual DNN model results in error reductions of 74% and 65%, compared with a single DNN and classical adaptive Kalman filter approaches, respectively. INDEX TERMS Adaptive filter, deep neural network, slip ratio, vehicle speed estimation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles

Kim

Yoon

2020

IEEE Access

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“…Deep learning and reinforcement learning have the ability to process large amounts of data from vehicle sensors and to identify highly nonlinear mapping between input and output. Some of the applications include parameter estimation of the longitudinal vehicle dynamics using neural network model [11], lateral state estimation using multiple neural network ensemble [12] and deep learning estimation for vehicle suspension system [13]. Although the data-driven methods show a good performance in modeling of highly nonlinear systems, they abandon the established knowledge of the system, the input-output mapping strongly depends on experimental data and it is hard to prove their numerical stability [3], [14], [15].…”

mentioning

confidence: 99%

Innovative Interaction Approach in IMM Filtering for Vehicle Motion Models With Unequal States Dimension

Zubaca

Stolz

Seeber

et al. 2022

IEEE Trans. Veh. Technol.

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Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper.

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Vehicle state parameter estimation based on improved adaptive observer

Wang,

Zhao,

Wan

et al. 2023

2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)

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Lateral State Estimation of Preceding Target Vehicle Based on Multiple Neural Network Ensemble

Cited by 4 publications

References 16 publications

Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles

Dual Deep Neural Network Based Adaptive Filter for Estimating Absolute Longitudinal Speed of Vehicles

Innovative Interaction Approach in IMM Filtering for Vehicle Motion Models With Unequal States Dimension

Vehicle state parameter estimation based on improved adaptive observer

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