Decision tree assisted EKF for vehicle slip angle estimation using inertial motion sensors

Coyte, James L.; Li, Boyuan; Du, Haiping; Li, Weihua; Stirling, David; Ros, Montserrat

doi:10.1109/ijcnn.2014.6889626

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…Formulate the adaptation algorithm (21) as a nonlinear feedback systems shown in Fig. 3 where v k will be linked to the error between the measured output Y k listed in (18) at time k and the one predicted according to the adaptation law Φ T k θ * k . B.…”

Section: Stability and Convergence Analysismentioning

confidence: 99%

“…This highlights the importance of input measurement matrices, Φ T i , being well-conditioned in the regression model. By observing (18), we can infer that the conditional number of Φ T is roughly equal to the ratio of it's (2, 1) and (2,2) components, since L f ≈ L r for a vehicle. In order to avoid a bad adaptation performance, we should add an additional condition of…”

Section: B Hyperstability Analysismentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

Liao

Borrelli

2019

IEEE Trans. Veh. Technol.

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Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.Index Terms-sideslip angle estimation, bank angle estimation, extended Kalman filter, recursive robust least square

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Section: Stability and Convergence Analysismentioning

confidence: 99%

Section: B Hyperstability Analysismentioning

confidence: 99%

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

Liao

Borrelli

2019

IEEE Trans. Veh. Technol.

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“…Therefore, it can help the autonomous system to better determine the driver's information, and finish the driving work perfectly. The indicators which represent the physical attributes of the driver and reproduces the mathematical expression of the driving process include: steering wheel Angle, speed and other parameters [67]. The behavior of the driver to operate the vehicle, which has strong adaptability, needed to be considered by multiple variables, such as Limb model, the distribution of eye focus area, the use of foot pedals, and so on.…”

Section: Behavior Modelmentioning

confidence: 99%

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

Zou

Zhang

et al. 2018

MATEC Web Conf.

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Abstract. The cooperative driving is a main direction of intelligent vehicle development since safety-critical tasks must be executed by human and autonomous system. An intelligent vehicle is equipped with a full range of sensors, which are larger and faster than the perception and computational ability of a human driver, while the latter has a comprehensive ability to adapt to unexpected events. According to their respective advantages, the cooperative driving between human and autonomous system can have new synergies. The emphasis of this paper is to survey the current state of the art of cooperative driving, with a specific focus on decision-making and motion planning levels and correlative algorithms. Such researches enable the autonomous system to compensate the human driver in dangerous or uncomfortable circumstance. This paper provides insights into the scope of decision-making and motion planning for cooperative driving, as well as the shortcomings and tendencies.

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“…Another field of big data is its application in individual autonomous vehicles for estimation, prediction and control purposes. Big data provides large amount of relevant information about the environment, with which the perception can be improved [2]. Moreover, big data have been used in the prediction of vehicle slip through the combination of individual measurements of the vehicle and database information [3].…”

Section: Introductionmentioning

confidence: 99%

Lateral Control Design for Autonomous Vehicles Using a Big Data-Based Approach

Fényes

Németh

Gáspár

2020

Lecture Notes in Mechanical Engineering

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In the paper an improved Model Predictive Control (MPC) design is presented for autonomous vehicles. The improvement of the control design is based on big data analysis of the lateral vehicle dynamics. In the big data analysis, the decision tree algorithm, C4.5 is used to determine the stable regions of the vehicle. Moreover, C4.5 is extended with the MetaCost algorithm, which is able to weight the percentages of certain misclassifications. In this way, the safe motion of the vehicle can be guaranteed. The results of the big data analysis are states-sets, which are used as constraints in the MPC control design.

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Decision tree assisted EKF for vehicle slip angle estimation using inertial motion sensors

Cited by 13 publications

References 15 publications

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

Lateral Control Design for Autonomous Vehicles Using a Big Data-Based Approach

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