A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Huang, Yu; Duan, Jianmin; Taheri, Saied; Cheng, Huan; Qi, Zhiquan

doi:10.1177/1687814015578361

Cited by 34 publications

(23 citation statements)

References 25 publications

(28 reference statements)

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“…Yu et al used unscented Kalman filter to estimate vehicle state and designed MPC controller based on the estimated vehicle state. 24 The simulation results verified the effectiveness and robustness of state estimation and path tracking. Ercan et al considered the dynamics of vehicle steering system and identified the parameters of steering system model online with recursive least squares algorithm.…”

Section: Introductionmentioning

confidence: 66%

Path tracking of autonomous vehicle based on adaptive model predictive control

Lin

Chen

Zhao

et al. 2019

International Journal of Advanced Robotic Systems

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.

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Section: Introductionmentioning

confidence: 66%

Path tracking of autonomous vehicle based on adaptive model predictive control

Lin

Chen

Zhao

et al. 2019

International Journal of Advanced Robotic Systems

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“…The diagram of hierarchical behavior representation in the numerical case. There are four high-level behaviors I, II, III and IV which are composed of three of the five stages A, B, C, D and E while each of these stages can also be separated into two of the six sub-stages (1)- (6). The order of stages and sub-stages are fixed in each high-level behavior.…”

Section: Generic Tracking and Prediction Frameworkmentioning

confidence: 99%

Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving

Zhan

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlusion. To overcome these challenges, we propose a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multi-modality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method. An example in this paper is a learning-based model for hierarchical time-series prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and prediction in highway scenarios. Instead of only focusing on forecasting trajectory of a single entity, we jointly predict continuous motions for interactive entities simultaneously. The proposed approaches are evaluated from multiple aspects, which demonstrate great potential for intelligent vehicular systems and traffic surveillance systems.Index Terms-Trajectory prediction, multi-target tracking, sequential Monte Carlo, probabilistic graphical models, deep learning, bahavior recognition.

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“…4. If the vehicle vibration, roll, pitch and other secondary factors are ignored, and the height of vehicle gravity center is h, steering angle is α, then the vehicle dynamics model is simplified as equation (8) to (13) (16,17) . − ( 1 + 2 ) + 3 + 4 (9) = 1 + 2 + 3 + 4 (10)…”

Section: Dynamics Model Of Vehiclementioning

confidence: 99%

Research on Electronic Brake Force Distribution and Anti-Lock Brake of Vehicle Based on Direct Drive Electro Hydraulic Actuator

Gong

Qian

Yan

2020

IJAE

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In order to effectively improve the vehicle safety, this paper proposed an electronic brake force distribution scheme and an anti-lock brake scheme for passenger car based on a novel brake-by-wire system. The brake-by-wire system was introduced at first, then the electronic brake force distribution scheme and anti-lock brake scheme are designed after analyzing of brake system and vehicle model. At last, the experiment is accomplished to prove the feasibility of the novel brake-by-wire actuator, and the co-simulations of vehicle based on Matlab/Simulink and AMESim are accomplished for typical braking process to prove the superiority of proposed brake-by-wire system and brake scheme. On the one hand, the experiment and simulation results show that the proposed electronic brake force distribution scheme is able to distribute the braking force according to braking intensity and conditions. So the β curve can as close as possible to the I curve. On the other hand, the anti-lock brake scheme is able to accurately regulate the wheel slip rate and adapt to different brake intensity. So the anti-lock brake scheme processes strong robust performance.KEY WORDS: brake-by-wire, linear motor, electric brake force distribution, anti-lock brake, fuzzy control (B1)

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A model predictive control approach combined unscented Kalman filter vehicle state estimation in intelligent vehicle trajectory tracking

Cited by 34 publications

References 25 publications

Path tracking of autonomous vehicle based on adaptive model predictive control

Path tracking of autonomous vehicle based on adaptive model predictive control

Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving

Research on Electronic Brake Force Distribution and Anti-Lock Brake of Vehicle Based on Direct Drive Electro Hydraulic Actuator

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