Vehicle lateral and longitudinal velocity estimation based on Adaptive Kalman Filter

Chu, Liang; Shi, Yanru; Zhang, Yongsheng; Liu, Hongwei; Xu, Mingfa

doi:10.1109/icacte.2010.5579565

Cited by 11 publications

(4 citation statements)

References 7 publications

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Section: Literature Reviewmentioning

confidence: 99%

“…In order to overcome CVS performance degradation caused by non-ideal communication, methods for tracking and predicting information have been proposed [30][31][32][33][34]. The constant speed or acceleration model is the most frequently accepted assumption for prediction by conventional vehicle manufacturers.…”

Section: Tracking and Predictionmentioning

confidence: 99%

“…This is likely due to the relative simplicity and robust nature of the filter itself. In [33] is presented an adaptive KF to predict vehicle velocity, the approach is tested in a variety of driving scenarios showing a good performance in velocity prediction. More recent effective approaches for trajectory prediction include recurrent neural networks [36,37], gaussian mixture models [38,39] and Long Short-Term Memory NNs [40,41].…”

Section: Tracking and Predictionmentioning

confidence: 99%

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Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

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Raftari

Mahjoub

et al. 2023

IET Intelligent Trans Sys

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Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems is proposed. The CA-TC consists of two modules: a Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on-the-fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Tracking and Predictionmentioning

confidence: 99%

Section: Tracking and Predictionmentioning

confidence: 99%

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Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

Valiente

Raftari

Mahjoub

et al. 2023

IET Intelligent Trans Sys

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“…To obtain the data for fitting, Chen used a hydraulic wear test system and cement terrain. The bicycle model can be seen to be a popular method for theoretical estimates of tire wear with the to evaluate the tire wear rate (Grosch, 2004) use of Kalman filters (Baffet et al, 2007;Baffet et al, 2008a;Baffet al, 2008b;Chu et al, 2010a;Chu et al, 2010b;Pan et al, 2009;Zhu & Zheng, 2008;Zhang et al, 2009), where in many studies the Kalman filters are used to estimate side slip angles, cornering stiffnesses, yaw rates, speeds, and other tire forces derived from a bicycle model to analyze tire wear. 2014) combine a FEA tire wear testing and neural network tools to estimate tire wear.…”

Section: Simulated Tire Wear Modelingmentioning

confidence: 99%

Tire Wear and Pollutants: An Overview of Research

El-Sayegh

2023

AAES

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Tire Road and Wear Particles are a major source of microplastic emissions. Tire and Road Wear Particles are important to study and understand as there are alarming amounts found in various environments. Currently, Tire and Road Wear Particles compared to other microplastics are not studied as rigorously in literature but are becoming a larger field of study due to their impact on emissions control. Tire Road and Wear Particles are commonly found as Styrene Butadiene Rubber Butadiene Rubber, and Natural Rubber To understand and quantify tire wear, experimental and mathematical models are developed to estimate tire wear. Tire wear can be measured experimentally using semi-empirical models, predetermined data, and sensor technologies. Tire wear is also measured mathematically using different modeling approaches and different friction models. This review discusses different and popular methodologies for estimating tire wear through experimental and simulated environments. Furthermore, discusses a review of the literature regarding tire wear emissions and its impact on the environment. Finally, it is evident that an accurate simulated tire wear model can be developed in the future alongside a driver model to predict tire wear emissions.

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An Innovative Robust $\mathcal{H}_{\infty}$ Adaptive Cruise Control Method with Input Constraint Considering Tire-road Friction Coefficient

Fang,

Liang,

Yin

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Vehicle lateral and longitudinal velocity estimation based on Adaptive Kalman Filter

Cited by 11 publications

References 7 publications

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

Tire Wear and Pollutants: An Overview of Research

An Innovative Robust $\mathcal{H}_{\infty}$ Adaptive Cruise Control Method with Input Constraint Considering Tire-road Friction Coefficient

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