Automatic Parking Control of Unmanned Vehicle Based on Switching Control Algorithm and Backstepping

Gao, Hongbo; Zhu, Juping; Li, Xinde; Yu, Ker‐Wei; Li, Jiehao; Su, Hang

doi:10.1109/tmech.2020.3037215

Cited by 52 publications

(21 citation statements)

References 24 publications

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“…However, the above methods are not applicable to scenarios when cyclists changing directions because they treat the cyclist as a rigid body and less consider the intention in cyclists' mind. Pool et al [13] modeled the rider's motion as a regular or a hybrid linear dynamic system [14,15]. The hybrid method used road intersection as features to distinguish the cyclist's motion intention such as going straight or making turns.…”

Section: Introductionmentioning

confidence: 99%

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

Gao

Cai

et al. 2021

Sci. China Inf. Sci.

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Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists' intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists' trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies.

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Section: Introductionmentioning

confidence: 99%

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

Gao

Cai

et al. 2021

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the vehicle industry, technologies for driver safety and convenience such as autonomous vehicles, intelligent vehicles, and advanced driver assistance systems are being actively researched [1][2][3]. Autonomous vehicles has garnered significant attention from society as well.…”

Section: Introductionmentioning

confidence: 99%

Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Jeon

Kim²,

Baek

2021

Sensors

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Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.

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“…At present, the inevitable trend of the development of manufacturing industry is intelligentization and informatization (Lim et al, 2020). Improving the level of intelligence in the automobile industry to comply with the development trend of intelligent driving can further promote the development of manufacturing industry (Gao et al, 2020a(Gao et al, , 2020b(Gao et al, , 2020c. The development of control algorithm restricts the progress of automobile industry.…”

Section: Introductionmentioning

confidence: 99%

Force tracking control of grinding end effector based on backstepping + PID

Dai

et al. 2021

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Purpose This study aims to realize the constant force grinding of automobile wheel hub. Design/methodology/approach A force control strategy of backstepping + proportion integration differentiation (PID) is proposed. The grinding end effector is installed on the flange of the robot. The robot controls the position and posture of the grinding end actuator and the grinding end actuator controls the grinding force output. First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. Finally, the feasibility of the proposed method is verified by simulation and experiment. Findings The simulation and experimental results show that the backstepping + PID strategy can track the expected force quickly, and improve the dynamic response performance of the system and the quality of grinding and polishing of automobile wheel hub. Research limitations/implications The mathematical model is based on the pneumatic system and ideal gas, and ignores the influence of friction in the working process of the cylinder, so the mathematical model proposed in this study has certain limitations. A new control strategy is proposed, which is not only used to control the grinding force of automobile wheels, but also promotes the development of industrial control. Social implications The automatic constant force grinding of automobile wheel hub is realized, and the manpower is liberated. Originality/value First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. The nonlinear model of the system is controlled by backstepping method, and in the process, the linear system composed of errors is obtained, and then the linear system is controlled by PID to realize the combination of backstepping and PID control.

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Automatic Parking Control of Unmanned Vehicle Based on Switching Control Algorithm and Backstepping

Cited by 52 publications

References 24 publications

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections

Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Force tracking control of grinding end effector based on backstepping + PID

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