Intelligent Vehicle Automatic Lane Changing Lateral Control Method based on Deep Learning

2020 ◽  
Author(s):  
Lu Jianglin
Keyword(s):  
Deep Learning ◽  
Control Method ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Lane Changing

scholarly journals A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

2014 ◽  
Vol 7 (1) ◽  
pp. 296209 ◽  
Author(s):  
Linhui Li ◽  
Hongxu Wang ◽  
Jing Lian ◽  
Xinli Ding ◽  
Wenping Cao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Fuzzy Neural

scholarly journals Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 228
Author(s):  
Tao Yang ◽  
Ziwen Bai ◽  
Zhiqiang Li ◽  
Nenglian Feng ◽  
Liqing Chen
Keyword(s):  
Control System ◽  
Vehicle Dynamics ◽  
Error Control ◽  
Control Method ◽  
Tracking Error ◽  
Error Model ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Perception System ◽  
Lqr Controller

Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

A Review on Lane Changing for Intelligent Vehicle

2015 ◽  
Vol 8 (3) ◽  
pp. 184-194 ◽  
Author(s):  
Ronghui Zhang ◽  
Fuliang Li ◽  
Xuecai Yu ◽  
Zhonghua Zhang ◽  
Feng You ◽  
...  
Keyword(s):  
Intelligent Vehicle ◽  
Lane Changing

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

scholarly journals Thermal station modelling and optimal control based on deep learning

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2965-2973
Author(s):  
Min Cao
Keyword(s):  
Deep Learning ◽  
Thermal Power Station ◽  
Control Method ◽  
Learning Algorithm ◽  
Thermal Power ◽  
Heating System ◽  
Utilization Rate ◽  
Power Station ◽  
Deep Learning Algorithm ◽  
Deterministic Strategy

To solve the mismatch between heating quantity and demand of thermal stations, an optimized control method based on depth deterministic strategy gradient was proposed in this paper. In this paper, long short-time memory deep learning algorithm is used to model the thermal power station, and then the depth deterministic strategy gradient control algorithm is used to solve the water supply flow sequence of the primary side of the thermal power station in combination with the operation mechanism of the central heating system. In this paper, a large number of historical working condition data of a thermal station are used to carry out simulation experiment, and the results show that the method is effective, which can realize the on-demand heating of the thermal station a certain extent and improve the utilization rate of heat.

scholarly journals Fuzzy Sliding Mode Lateral Control of Intelligent Vehicle Based on Vision

2013 ◽  
Vol 5 ◽  
pp. 216862 ◽  
Author(s):  
Linhui Li ◽  
Jing Lian ◽  
Mengmeng Wang ◽  
Ming Li
Keyword(s):  
Sliding Mode ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Fuzzy Sliding Mode

A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

2018 ◽  
Vol 25 (4) ◽  
pp. 74-81 ◽  
Author(s):  
Bomin Mao ◽  
Fengxiao Tang ◽  
Zubair Md. Fadlullah ◽  
Nei Kato ◽  
Osamu Akashi ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Deep Learning ◽  
Network Traffic ◽  
Traffic Control ◽  
Control Method

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Sign in / Sign up

Export Citation Format

Share Document